{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Capital Bikeshare 数据探索\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先导入必要的工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O\n",
    "from sklearn.metrics import r2_score  #评价回归预测模型的性能\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"day.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "观察year，发现是2011年的，好奇有没有2012年的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>727</td>\n",
       "      <td>2012-12-27</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.254167</td>\n",
       "      <td>0.226642</td>\n",
       "      <td>0.652917</td>\n",
       "      <td>0.350133</td>\n",
       "      <td>247</td>\n",
       "      <td>1867</td>\n",
       "      <td>2114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>728</td>\n",
       "      <td>2012-12-28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.253333</td>\n",
       "      <td>0.255046</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>0.155471</td>\n",
       "      <td>644</td>\n",
       "      <td>2451</td>\n",
       "      <td>3095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>729</td>\n",
       "      <td>2012-12-29</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.253333</td>\n",
       "      <td>0.242400</td>\n",
       "      <td>0.752917</td>\n",
       "      <td>0.124383</td>\n",
       "      <td>159</td>\n",
       "      <td>1182</td>\n",
       "      <td>1341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>730</td>\n",
       "      <td>2012-12-30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.255833</td>\n",
       "      <td>0.231700</td>\n",
       "      <td>0.483333</td>\n",
       "      <td>0.350754</td>\n",
       "      <td>364</td>\n",
       "      <td>1432</td>\n",
       "      <td>1796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>731</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.215833</td>\n",
       "      <td>0.223487</td>\n",
       "      <td>0.577500</td>\n",
       "      <td>0.154846</td>\n",
       "      <td>439</td>\n",
       "      <td>2290</td>\n",
       "      <td>2729</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "726      727  2012-12-27       1   1    12        0        4           1   \n",
       "727      728  2012-12-28       1   1    12        0        5           1   \n",
       "728      729  2012-12-29       1   1    12        0        6           0   \n",
       "729      730  2012-12-30       1   1    12        0        0           0   \n",
       "730      731  2012-12-31       1   1    12        0        1           1   \n",
       "\n",
       "     weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "726           2  0.254167  0.226642  0.652917   0.350133     247        1867   \n",
       "727           2  0.253333  0.255046  0.590000   0.155471     644        2451   \n",
       "728           2  0.253333  0.242400  0.752917   0.124383     159        1182   \n",
       "729           1  0.255833  0.231700  0.483333   0.350754     364        1432   \n",
       "730           2  0.215833  0.223487  0.577500   0.154846     439        2290   \n",
       "\n",
       "      cnt  \n",
       "726  2114  \n",
       "727  3095  \n",
       "728  1341  \n",
       "729  1796  \n",
       "730  2729  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "确实有，而且是一整年的，于是进行数据分割，2011年的应该用于训练，2012年的用于测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>361</td>\n",
       "      <td>2011-12-27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>0.327633</td>\n",
       "      <td>0.762500</td>\n",
       "      <td>0.188450</td>\n",
       "      <td>103</td>\n",
       "      <td>1059</td>\n",
       "      <td>1162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>362</td>\n",
       "      <td>2011-12-28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.299130</td>\n",
       "      <td>0.279974</td>\n",
       "      <td>0.503913</td>\n",
       "      <td>0.293961</td>\n",
       "      <td>255</td>\n",
       "      <td>2047</td>\n",
       "      <td>2302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>363</td>\n",
       "      <td>2011-12-29</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.248333</td>\n",
       "      <td>0.263892</td>\n",
       "      <td>0.574167</td>\n",
       "      <td>0.119412</td>\n",
       "      <td>254</td>\n",
       "      <td>2169</td>\n",
       "      <td>2423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>364</td>\n",
       "      <td>2011-12-30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.311667</td>\n",
       "      <td>0.318812</td>\n",
       "      <td>0.636667</td>\n",
       "      <td>0.134337</td>\n",
       "      <td>491</td>\n",
       "      <td>2508</td>\n",
       "      <td>2999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>365</td>\n",
       "      <td>2011-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>0.414121</td>\n",
       "      <td>0.615833</td>\n",
       "      <td>0.220154</td>\n",
       "      <td>665</td>\n",
       "      <td>1820</td>\n",
       "      <td>2485</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "360      361  2011-12-27       1   0    12        0        2           1   \n",
       "361      362  2011-12-28       1   0    12        0        3           1   \n",
       "362      363  2011-12-29       1   0    12        0        4           1   \n",
       "363      364  2011-12-30       1   0    12        0        5           1   \n",
       "364      365  2011-12-31       1   0    12        0        6           0   \n",
       "\n",
       "     weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "360           2  0.325000  0.327633  0.762500   0.188450     103        1059   \n",
       "361           1  0.299130  0.279974  0.503913   0.293961     255        2047   \n",
       "362           1  0.248333  0.263892  0.574167   0.119412     254        2169   \n",
       "363           1  0.311667  0.318812  0.636667   0.134337     491        2508   \n",
       "364           1  0.410000  0.414121  0.615833   0.220154     665        1820   \n",
       "\n",
       "      cnt  \n",
       "360  1162  \n",
       "361  2302  \n",
       "362  2423  \n",
       "363  2999  \n",
       "364  2485  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data[data[\"yr\"]==0]  ##筛选出yr这一列等于0的那些行\n",
    "data.tail()  ##验证一下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 365 entries, 0 to 364\n",
      "Data columns (total 16 columns):\n",
      "instant       365 non-null int64\n",
      "dteday        365 non-null object\n",
      "season        365 non-null int64\n",
      "yr            365 non-null int64\n",
      "mnth          365 non-null int64\n",
      "holiday       365 non-null int64\n",
      "weekday       365 non-null int64\n",
      "workingday    365 non-null int64\n",
      "weathersit    365 non-null int64\n",
      "temp          365 non-null float64\n",
      "atemp         365 non-null float64\n",
      "hum           365 non-null float64\n",
      "windspeed     365 non-null float64\n",
      "casual        365 non-null int64\n",
      "registered    365 non-null int64\n",
      "cnt           365 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 48.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "instant       0\n",
       "dteday        0\n",
       "season        0\n",
       "yr            0\n",
       "mnth          0\n",
       "holiday       0\n",
       "weekday       0\n",
       "workingday    0\n",
       "weathersit    0\n",
       "temp          0\n",
       "atemp         0\n",
       "hum           0\n",
       "windspeed     0\n",
       "casual        0\n",
       "registered    0\n",
       "cnt           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "探索数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.0</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>183.000000</td>\n",
       "      <td>2.498630</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.526027</td>\n",
       "      <td>0.027397</td>\n",
       "      <td>3.008219</td>\n",
       "      <td>0.684932</td>\n",
       "      <td>1.421918</td>\n",
       "      <td>0.486665</td>\n",
       "      <td>0.466835</td>\n",
       "      <td>0.643665</td>\n",
       "      <td>0.191403</td>\n",
       "      <td>677.402740</td>\n",
       "      <td>2728.358904</td>\n",
       "      <td>3405.761644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>105.510663</td>\n",
       "      <td>1.110946</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.452584</td>\n",
       "      <td>0.163462</td>\n",
       "      <td>2.006155</td>\n",
       "      <td>0.465181</td>\n",
       "      <td>0.571831</td>\n",
       "      <td>0.189596</td>\n",
       "      <td>0.168836</td>\n",
       "      <td>0.148744</td>\n",
       "      <td>0.076890</td>\n",
       "      <td>556.269121</td>\n",
       "      <td>1060.110413</td>\n",
       "      <td>1378.753666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>416.000000</td>\n",
       "      <td>431.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>92.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>0.321954</td>\n",
       "      <td>0.538333</td>\n",
       "      <td>0.135583</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>1730.000000</td>\n",
       "      <td>2132.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>183.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.479167</td>\n",
       "      <td>0.472846</td>\n",
       "      <td>0.647500</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>2915.000000</td>\n",
       "      <td>3740.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>274.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.656667</td>\n",
       "      <td>0.612379</td>\n",
       "      <td>0.742083</td>\n",
       "      <td>0.235075</td>\n",
       "      <td>871.000000</td>\n",
       "      <td>3632.000000</td>\n",
       "      <td>4586.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>365.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.849167</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3065.000000</td>\n",
       "      <td>4614.000000</td>\n",
       "      <td>6043.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season     yr        mnth     holiday     weekday  \\\n",
       "count  365.000000  365.000000  365.0  365.000000  365.000000  365.000000   \n",
       "mean   183.000000    2.498630    0.0    6.526027    0.027397    3.008219   \n",
       "std    105.510663    1.110946    0.0    3.452584    0.163462    2.006155   \n",
       "min      1.000000    1.000000    0.0    1.000000    0.000000    0.000000   \n",
       "25%     92.000000    2.000000    0.0    4.000000    0.000000    1.000000   \n",
       "50%    183.000000    3.000000    0.0    7.000000    0.000000    3.000000   \n",
       "75%    274.000000    3.000000    0.0   10.000000    0.000000    5.000000   \n",
       "max    365.000000    4.000000    0.0   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  365.000000  365.000000  365.000000  365.000000  365.000000  365.000000   \n",
       "mean     0.684932    1.421918    0.486665    0.466835    0.643665    0.191403   \n",
       "std      0.465181    0.571831    0.189596    0.168836    0.148744    0.076890   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.325000    0.321954    0.538333    0.135583   \n",
       "50%      1.000000    1.000000    0.479167    0.472846    0.647500    0.186900   \n",
       "75%      1.000000    2.000000    0.656667    0.612379    0.742083    0.235075   \n",
       "max      1.000000    3.000000    0.849167    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   365.000000   365.000000   365.000000  \n",
       "mean    677.402740  2728.358904  3405.761644  \n",
       "std     556.269121  1060.110413  1378.753666  \n",
       "min       9.000000   416.000000   431.000000  \n",
       "25%     222.000000  1730.000000  2132.000000  \n",
       "50%     614.000000  2915.000000  3740.000000  \n",
       "75%     871.000000  3632.000000  4586.000000  \n",
       "max    3065.000000  4614.000000  6043.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 各属性的统计特性\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.0</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>183.000000</td>\n",
       "      <td>2.498630</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.526027</td>\n",
       "      <td>0.027397</td>\n",
       "      <td>3.008219</td>\n",
       "      <td>0.684932</td>\n",
       "      <td>1.421918</td>\n",
       "      <td>0.486665</td>\n",
       "      <td>0.466835</td>\n",
       "      <td>0.643665</td>\n",
       "      <td>0.191403</td>\n",
       "      <td>677.402740</td>\n",
       "      <td>2728.358904</td>\n",
       "      <td>3405.761644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>105.510663</td>\n",
       "      <td>1.110946</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.452584</td>\n",
       "      <td>0.163462</td>\n",
       "      <td>2.006155</td>\n",
       "      <td>0.465181</td>\n",
       "      <td>0.571831</td>\n",
       "      <td>0.189596</td>\n",
       "      <td>0.168836</td>\n",
       "      <td>0.148744</td>\n",
       "      <td>0.076890</td>\n",
       "      <td>556.269121</td>\n",
       "      <td>1060.110413</td>\n",
       "      <td>1378.753666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>416.000000</td>\n",
       "      <td>431.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>92.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>0.321954</td>\n",
       "      <td>0.538333</td>\n",
       "      <td>0.135583</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>1730.000000</td>\n",
       "      <td>2132.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>183.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.479167</td>\n",
       "      <td>0.472846</td>\n",
       "      <td>0.647500</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>2915.000000</td>\n",
       "      <td>3740.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>274.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.656667</td>\n",
       "      <td>0.612379</td>\n",
       "      <td>0.742083</td>\n",
       "      <td>0.235075</td>\n",
       "      <td>871.000000</td>\n",
       "      <td>3632.000000</td>\n",
       "      <td>4586.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>365.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.849167</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3065.000000</td>\n",
       "      <td>4614.000000</td>\n",
       "      <td>6043.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season     yr        mnth     holiday     weekday  \\\n",
       "count  365.000000  365.000000  365.0  365.000000  365.000000  365.000000   \n",
       "mean   183.000000    2.498630    0.0    6.526027    0.027397    3.008219   \n",
       "std    105.510663    1.110946    0.0    3.452584    0.163462    2.006155   \n",
       "min      1.000000    1.000000    0.0    1.000000    0.000000    0.000000   \n",
       "25%     92.000000    2.000000    0.0    4.000000    0.000000    1.000000   \n",
       "50%    183.000000    3.000000    0.0    7.000000    0.000000    3.000000   \n",
       "75%    274.000000    3.000000    0.0   10.000000    0.000000    5.000000   \n",
       "max    365.000000    4.000000    0.0   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  365.000000  365.000000  365.000000  365.000000  365.000000  365.000000   \n",
       "mean     0.684932    1.421918    0.486665    0.466835    0.643665    0.191403   \n",
       "std      0.465181    0.571831    0.189596    0.168836    0.148744    0.076890   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.325000    0.321954    0.538333    0.135583   \n",
       "50%      1.000000    1.000000    0.479167    0.472846    0.647500    0.186900   \n",
       "75%      1.000000    2.000000    0.656667    0.612379    0.742083    0.235075   \n",
       "max      1.000000    3.000000    0.849167    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   365.000000   365.000000   365.000000  \n",
       "mean    677.402740  2728.358904  3405.761644  \n",
       "std     556.269121  1060.110413  1378.753666  \n",
       "min       9.000000   416.000000   431.000000  \n",
       "25%     222.000000  1730.000000  2132.000000  \n",
       "50%     614.000000  2915.000000  3740.000000  \n",
       "75%     871.000000  3632.000000  4586.000000  \n",
       "max    3065.000000  4614.000000  6043.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "记录号这种东西当然是无关的了，应该不考虑此特征，同理，year也应该不考虑，因为在此训练集当中，都是同一年。\n",
    "日期和月份和季节应该是类别特征，也应该不考虑\n",
    "data.drop(['instant'],axis=1,inplace=True)\n",
    "data.drop(['dteday'],axis=1,inplace=True)\n",
    "data.drop(['season'],axis=1,inplace=True)\n",
    "data.drop(['yr'],axis=1,inplace=True)\n",
    "data.drop(['mnth'],axis=1,inplace=True)\n",
    "测试了删除这些列和不删除这些列的结果，似乎对数据探测没有什么影响，所以这里决定不删除，后续根据预测结果再考虑删除看看有没有影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data.drop(['instant'],axis=1,inplace=True)\n",
    "#data.drop(['dteday'],axis=1,inplace=True) \n",
    "#data.drop(['season'],axis=1,inplace=True)\n",
    "#data.drop(['yr'],axis=1,inplace=True) \n",
    "#data.drop(['mnth'],axis=1,inplace=True) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "单变量分布分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88f2996b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 目标y（cnt）的直方图／分布\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.cnt.values, bins=30, kde=True)\n",
    "plt.xlabel('cnt value of bike shared', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88f09143c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 单个特征散点图\n",
    "plt.scatter(range(data.shape[0]), data[\"cnt\"].values,color='purple')\n",
    "plt.title(\"Distribution of cnt\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在2000及5000左右的数据较为集中，感觉有些点是离群点，比如y<800和y>5500的点应该删掉"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[data.cnt > 800]\n",
    "data = data[data.cnt < 5500]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "单个变量进行直方图绘制分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88f089f4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data.holiday, order=[0, 1]);\n",
    "plt.xlabel('holiday');\n",
    "plt.ylabel('Number of holiday');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "holiday,但这里没说0是holiday还是1是holiday，猜测应该是1是holiday？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'number of weekday')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88f07bcd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data.weekday);\n",
    "plt.xlabel('index of weekday');\n",
    "plt.ylabel('number of weekday')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "感觉不是一个很有指导意义的特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88f0879e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data.workingday, order=[0, 1]);\n",
    "plt.xlabel('workingday');\n",
    "plt.ylabel('Number of workingday');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这个工作日分布和上面的应该是一个感觉"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88f0772828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.weathersit.values, bins=30, kde=True)\n",
    "plt.xlabel('weathersit value', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88f06a2ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.temp.values, bins=30, kde=True)\n",
    "plt.xlabel('temp value ', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88f2998b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.atemp.values, bins=30, kde=True)\n",
    "plt.xlabel('atemp value of bike shared', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "和上面的图相比较，都在0.7摄氏度左右数值较大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88f089a630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.hum.values, bins=30, kde=True)\n",
    "plt.xlabel('hum value of bike shared', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "湿度也是0.7左右数值较大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88f089ff60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.windspeed.values, bins=30, kde=True)\n",
    "plt.xlabel('windspeed value of bike shared', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "风速在0.15到0.23左右，数值较大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88f06671d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.casual.values, bins=30, kde=True)\n",
    "plt.xlabel('casual value', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "感觉是非注册用户数在达到200和700左右的时候数值较大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88f05873c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.registered.values, bins=30, kde=True)\n",
    "plt.xlabel('registered value', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注册用户数在达到1500和4000左右数值较大"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "两两特征之间相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get the names of all the columns\n",
    "cols=data.columns \n",
    "\n",
    "# Calculates pearson co-efficient for all combinations，通常认为相关系数大于0.5的为强相关\n",
    "data_corr = data.corr().abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15, 15)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_corr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88f04b0be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "\n",
    "# Mask unimportant features\n",
    "sns.heatmap(data_corr, mask=data_corr < 1, cbar=False)\n",
    "\n",
    "plt.savefig('bike_corr.png' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "据图上数据来看，工作日和非注册用户数有强相关性，季节和湿度有强相关性，温度、体感温度和非注册用户数也有强相关性，\n",
    "值得注意的是，注册用户数和最终的y有着0.93的相关性，可以推论注册用户数是强烈影响着最终的结果，其次是温度和体感温度、非注册用户数对y有着很大的影响。\n",
    "另外，temp和atemp应该只保留一个，他们的相关性达到了完全相等的程度。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "instant and yr = 1.00\n",
      "weathersit and temp = 1.00\n",
      "casual and registered = 0.93\n",
      "dteday and yr = 0.84\n",
      "instant and dteday = 0.83\n",
      "temp and registered = 0.78\n",
      "weathersit and registered = 0.77\n",
      "temp and casual = 0.70\n",
      "weathersit and casual = 0.69\n",
      "windspeed and registered = 0.69\n",
      "workingday and atemp = 0.62\n",
      "dteday and casual = 0.58\n",
      "temp and windspeed = 0.58\n",
      "weathersit and windspeed = 0.58\n",
      "weekday and windspeed = 0.56\n",
      "dteday and registered = 0.55\n",
      "yr and casual = 0.51\n",
      "instant and casual = 0.51\n"
     ]
    }
   ],
   "source": [
    "#Set the threshold to select only highly correlated attributes\n",
    "threshold = 0.5\n",
    "# List of pairs along with correlation above threshold\n",
    "corr_list = []\n",
    "#size = data.shape[1]\n",
    "size = data_corr.shape[0]\n",
    "\n",
    "#Search for the highly correlated pairs\n",
    "for i in range(0, size): #for 'size' features\n",
    "    for j in range(i+1,size): #avoid repetition\n",
    "        if (data_corr.iloc[i,j] >= threshold and data_corr.iloc[i,j] < 1) or (data_corr.iloc[i,j] < 0 and data_corr.iloc[i,j] <= -threshold):\n",
    "            corr_list.append([data_corr.iloc[i,j],i,j]) #store correlation and columns index\n",
    "\n",
    "#Sort to show higher ones first            \n",
    "s_corr_list = sorted(corr_list,key=lambda x: -abs(x[0]))\n",
    "\n",
    "#Print correlations and column names\n",
    "for v,i,j in s_corr_list:\n",
    "    print (\"%s and %s = %.2f\" % (cols[i],cols[j],v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88f059b2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88e97d2ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88e97aec18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88e9756dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88e9756cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88f08a6f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88e95e25f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88f04aa9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88f0755da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88f08544e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88f068dc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88f07d3630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88f07d9da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88e932a710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88f048ba58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0Q4iCpEFNp/X8PXP6eloymdUL4mjXr9TOvQ/T13bMhLVMpfV8s0Dq84WmHQk2kQv8QSeo76eS6oG1OmrPX3812N9T03/Tbjql6gHz1kdH+JMPVZeAKQvKsfGptqMKgwqSRgN5lKnB2vUrtXPvw9DqOdcTYH7V8UXzB/ja+jP5wm+fzmhxkgOT8qe1i0yRIhf4g8docTKU7wdmm7IeuvIX2LR1J995fvf0YFlp9osjA0gtX1ZPoVD2f1WYo/yZ/0fveSLQOdYydY1NTAUy3TUzWUaV8isK/2ir976VvoY953qm0i9edjobv7SdjedWP18rWRjQrJwz5GNrhARbd1IquZb8II18bJvWHD8t5HyiHiz9PgQdMKemSuweG2dDk2CLegJjwWAfe8YmWL/lBxx+yABXnrOcpQsHZwWQBAkw6cT51/t+HD6wKKgXXLT998/jjX3F2IVxhlCuSCGq8QMRwpaFqVWyZm5vYfp9K2a/sAN1kNyLPj09Bd7u1SJrtP9GuRoXDvVzx6Wr6kYjQu0EwXEvoobm51/v+63c+ySol6+yUIClCwe1bi0G5GMTuaJVP0h1sEBPT2HW+iufZv6fJNIiBcmm0UgoFwpGycH6OqmtspgBI60lger1a05vz/S6wEq0bq19ZIoUuSPKci+t+H/iCD9vhWb9aFjShXSa9ZqR1lI/9frVyFwcdB1ljpApUoh6tGvWqt5X2NImaUmL1Kyqc6OSLv7rtJn1mhHlvY+Sev2qZwb3faBduEg7EmSKFJmiEyU9wiTRLZUco56vp5JOmJeaLUNoZLpLq1kvTUT1LFY/X/snS5kzA6cNmSJFZmiUlqlak+qEScrv35bHX5yVGSStM+5G1ymtZr00EGfqq3ZTtGUYmSJF91EZ0LDmpCVcePJRfPSeJwJH9MUtWCr7N7JrlE1rjmd48TzGxicZ6k9nBolGpru0mvXSQKOI03avVbOq36I5MkWKzFDpu6qs79Usom/R/AFGi5NgxGq+rOzf1idfYfVNj7HiugcZ6nBapCjNt6ruXCZOP6rMwO0j8S8yQ+VMtllaqcrcgXFnDqnVP59Oz7SjNJl1eeb5GcR5r1sJWBIzkcYmMkPlTLZRouDKtWeNNLs4+5eWmXaU69GyuLYtLuK+12Grfteim7VraWwiM1TOZOf2lStMV6eV8gcWP8y93YTBrfYvLTPtKE1maVnGkAbC3Gs/CGduX4Gx8SmGvIX/YWr+haXbtWtpbCJT+DPZnsLBtFLVoeyVg87+hCsSRzHTjpIoKzJnrbpz3BpLkHvtC5g7v/08//rmAS7f/ATLr32QO7/9fKzZabpdu5ZgE5ml0cDifzbYnz7zYDuEHayjNJml0dRaj+q0Znd++/mOmOV8AVNdQy9szb+wdLt2LVOkyDW+9nbHpatmZKnPIq2Yl6I0j6bR1FqPoEtD2ul7kHV+voCpNonHbSJPYyBTkkhjE13BaHGKyzZ7Zp+IC1AmRVjzkq/dTS97dbRtHk2bqbUeQZeGtErQRNe+gKkOdmoU/NROn3yttGBwy9qVmdCu40CCTeSevPgbwpiXkqgwkGYq/YFhtaMg5t6gz5Rvvn3o6Vf5zAcPVkV/6OlXufmi6ARP9f3+yF3b6O8pcPslrVV1zzr510lF15MXf0Mz81JlYVAgtswYSdNKaq/KBNC+dhTELBfU3Bv0mfLNt7915nHM7Stw2yWnzoiKjMqsWysTyu984fvcvm7VtHbdTUhjE7kna9F89WgUvFEqOfYemGDP6DiXbd7G3JwI81Y1z0p/4PDiIW4OaJYLqomFeaYqI3nnz+mbVfMvCrNuI0HbjevZukuMi66kWfmWrNAoeGNfcZI3xya45oGnyrkqQ2gpaaadnIyVuS6DVByH4JpYq89UXIml62nzByamGC1Odd16NmX3F11B3jPVl5zDOVhxXTkrfJKpxOK8tklnug9TJDbsecdaEaDOvuf29XDZ5sbnk6LfhrL7C1FJsx9n3jPVj41P8cbe4vSsfeuTrwDwR7/6LpYuHIxtwIo7w0XSYethNLGwz1ScFQGqtfkDE1PTy1saaaB5zVAiH5vIPN0eAQjlAfmwwT7+5EMHI+927S0yNNATKsw/rD8m7ojTpBeFNyvO2g5xBzFNC1p3cHnLc681XlbQ6P5l2TeXv6mr6DrinAlnhULBmD+nj77ecoi3vxB9sD+4ltbK7D2JwTrpReFxafdJaZ+Vv4dbHx3hMx88cZZJ2p8Y+PdvzUlLuOKsYYYXz2Pk9X3M6S1kWpOTxiYyT17C+dulUDAG+3uZN8eLtJsTLtKuFe0riYjTrCwKb0ZS2md1XcAbvvEMm9YcX1MDHRufYv3Zw3zsvBVs2rqTFdc9yKatO8vtGV77GZtgM7M5ZvYvZvakme00sz/w2u8ysxfMbLv3t9JrNzO7xcxGzGyHmZ1Ssa91Zvac97curj6LbJKXcP5O08oEIUv5IztNnGbOSqp/D1uffGVaWFVPDAb7erj0jGNnZWYZGujN9GQxTo2tCJztnDsJWAmcb2bv8T67yjm30vvb7rVdACzz/i4HPgtgZguATwKnA6cBnzSzw2Lst8gASh8UPa1MEJIarLNCM79UEtpnmMlGoVDW7KuFWBwpv5IkNgeEK68j2Oe97fP+GnkfPwBs9r73XTM71MyOAN4HPOyc2wNgZg8D5wNb4uq7SDe1fEGfu/iUg76lHIbzJ0GziMB6kad5jzgNgp/1ZbQ4yfqqGoFJC/qwfslavr+Hnn61Yb3DtBPrOjYz6wGeAIaBW51zV5vZXcB7KWt03wQ+4ZwrmtlXgT92zv2j991vAldTFmxznHOf9tr/K7DfOXdD1bEup6zpsXTp0lNffPHF2M5LdJYwa43iJkVrgCKh3vnkNSw8CvxrM1qcnF4g79Op5zIM9e7tgsG+2Aqh1iGynccaPOKcm3LOrQSOAk4zsxOAa4B/A7wbWEBZeEHtk3IN2quPdZtzbpVzbtWiRYsi6b9IJ2kIFimVHGPjk+weLeZqmUE9U1lWE0knEbLuX5ujFwx2/LlshXrm5ChTfiVNIlGRzrmfAn8PnO+ce9WVKQKfp+w3A3gZOLria0cBrzRoF11KlMEirQx8/gz39Z8VWb8lvmKRaSINk4mwJLW+0b82WfZL5SXy1CfOqMhFZnao93oucA7wvzy/GWZmwIXA095XtgKXeNGR7wHecs69CjwEnGdmh3lBI+d5bSLn1BM6UUXi1Rv4pqZKDYVd1mforZDFyNOktEz/2vhrxhTE1HniNPweAdzt+dkKwJedc181s2+Z2SLKJsbtwO94238deD8wAowBvwngnNtjZn8IfM/b7lN+IInIL818OlEs2q21sHvL4y9y0elLZznNK31J1TP0rCcaDkIWE0lXapmVC5D3j5ezakSllVRemxsffuZgGrOQC+SjIm9+31ZQEmSRSuIKEKn80QOzEuw+dOUvsGnrzobH9fu2aP5ALImG0zowdbJfrRw77vvUbv/iIONBPtkIHhGiVeLw6VSbHn+ye2yWeS1ItWV/hr5rb3F6hv7s9Rdw+yXtr+FKc97LTvlhWr0m/n3aeO7yWQuQozZJpsVHldUgn6iRYBOpJA6fTvWP/saHn52RNPi9xy1ktDjZ9LiVptAbf30lb58/UE40HDKFVZA+duvAVEmr18S/T0sXdo8vtJ0JYZaTHleTL0eAyA1x+HSqf/Rbn3yFgjFjYffc3kKg48a1KHmwv4fDDxngoSt/YToh7Wf/fiSXg3BQ2hms/SKs3eILbTXRcsZNmLOQj02klqj9FkH9dp30l4yNT7JndJyr7jvoD/qTD53IgqF+BvvzNQgHpV1/a94G7UaEPVf/WcfRtCBpAkR2MyTYRNeQhQFu34HJ2gPMJauYN6fxjDsNwQtx9CWK+5am6xM3jc618rMDEwdTgH3ht0+frr7uE2el8jqogrYQEG7ASqq2VzuDaN2KxwOzzW5+fkIcjI53Pkeh36eoJw9R3LduymdZ71yr780jG39xOgVY3pauKHhEZJZWouXijl5rN6oxaNBMqeTYe2CCPaPjvL43PRlQ4gp+SUvUYZapvjeVCQbytrhcgk2kkkYRWv5naSyG2O7AHjSrytjEFG+OTXDVfTtSlQEli6m3uoXqe1OZAswvSDq9dCXj5Yck2ETqaKT1VH42N6JBNMow56ADe71jBq1vNtjfMy3Q0pSjMIupt4KQh1D46ntz66MjM5a77NpbZGigt7x0JeNasQSbSB2NtJ7Kz6IY0FsxHTYa5IIM7JXH3Pil7byxtwhWDhzxUz01M7uNjU/x0p6x1OUozGNF7XbMy2kSiNX3ZtfeIvMHern9kvwViVVUpEgdJedmpbrqLRjPfPp8zGz6szUnLWk7VVLYUPJmwRFBgieiSPXk+9j2Fie56r4dHH7IAFees3w6R+HcvkLStbRm9C1PEYiV98vPN/nSnjEWHzJQdwlGmgqPVvcrxfdG4f6NkGDLNrWEzcZzlnHR6UsZK07NKOa45qQlbDzXG9Bb+KHWE6L1wpyDCMJmg4d/zK+tP7NpXsqGffcGz1KpHE3pJ90FUr+sIUuUnGPjl7az8dzqSchKFg4NzLqmWS882kGUK1Lkl1rmrEvPOJYNW7Zz48PPzjC7tesXCOsTCuJDa2ZK9I8ZJC9lIwoFY7C/t5zKy2w6pVezAJYkzGNpMsG1y9j4FFeeUyvf5PaaQUHdWNYobWjaIFJHzXVLnkDxNatNa46fLkHSTmmQsKm7Wk1ZVOuYvo8s6rVDjYRvEovUs7AQPgyDfT2h8k12Y1mjtCGNTaSSaq2nUrPa+uQrrL7pMS7+q8fBaGuwDBqF6BNFcIR/zMWHDHDL2pWRB1o00kKTSLKct0TOhYIxVgyu2avwaOeRj01kgjRpAUEd8EG2i8OZ3+haYbNr0EWdOims37JThLn2YZ6/ym2rg3o6UXg0Qyh4pBESbPkk5RFdM+i0IK53reIq4FpJEsdo91lo5f6EFYRZeVZThIJHRPeR1rRKtQIl6pnjDkxOJRJUUe9aJbHOLO5jRFGMtRVzaZjnL63ParcgD6YQbVB35j+vf1awweGHDHR8XVMSiaDjPkalUAKmhdIdl66i5Ah0TKX+yjfS2IRog3oz/1qVuK88Z3kqkhUnoU3EeYxaQsmfNATV4vKa+kuUkWATXUsUa60azfyrzXFhQsZFfWoJpbCThjym/hIHkSlSdCVRBXfUW9e2f6I0yxznh4ynbV1T1gIdaq09DDtpqGcuhXLwS1auRRiydp/bQVGRoiuJKnKv1TDwTi9ZiKtPSQ2e1cfBUbvyeIj7mcb7ExW1zu1zF59CT6FQTseWDkGncP9GSLCJZkS51ioLYeBJhP93UjBEcewklim0QhTPTPW5rTlpCR8/fwVX3dd6AvEYULi/EO0QZfBAp8PAm/kKG4XHRxkd2MmMI2EzyNQijZGSUSxtgNnndsVZw1x1X3Xuy2TzicaJBJvoSvISPBBk4GtY3y5CAR+HYAgzwLY7aUhjpGRUk4Xqc2uUgDsqYdpJJNhEVxLFDD8NBBn4GgmcKAV81IIh6QG23rWY21vomPYS1WSh+tz8BNyVJJlPNG4UFSlySzPfhD/DBzriQ4nCdxJk4GtWkSCq6MBGlRJaOdd6C7Hj8nnVipSc21tgz9hExwJKoqgmAbPP7cDEFLesXTkrWcBgXw8YqTPJhkWCTeSStEe4xb3coHLga1aap1rAt9q3RiH0reyvEz6v6muxrziZqHCtJmxZpUZUnttgfy9zentqZofZ5yUXSNuylDAoKlLkklYj3JKKWkx6uUGY84o6OrDV/aUhSjENlQqSjqTt4KQwsp1nQ/wKEZJWZvtJ/qCj0kaC5mUMY3aNWlNqdX9BtJW4B/2oTIHtkLTJPIl8onGj4BGRS1oJZEjSad6p5QZJ962d/TUL8EkiuCQv0bNhyXp1ApkiRS5pqd5WgmanNPsA48hGEse5JmWq7KZUVB1GmUcaIcEmIPyAlLRPJ80DZtR9i6VSeAr8XyJSlHlEiGaENackbXZKs7kn6r7Fca5pXFAt0oE0NiEqSLMWJWaSZnOuaIn0myLNbA7wGDBAOfryb5xznzSzY4F7gQXA94EPO+fGzWwA2AwaMiNuAAAgAElEQVScCuwGft0592NvX9cAHwGmgPXOuYcaHVuCTYjuQBORXJEJU2QRONs5dxKwEjjfzN4DfAb4U+fcMuBNygIL7/+bzrlh4E+97TCzdwIXAccD5wN/YWb5DkkSuSHryWTTTprNuaJzxCbYXJl93ts+788BZwN/47XfDVzovf6A9x7v818yM/Pa73XOFZ1zLwAjwGlx9VuIqMhDMlkhskiswSNm1mNm24HXgYeB/w381Dk36W3yMnCk9/pI4CUA7/O3gIWV7TW+I0RLJKFJ5SGZrBBZJFbB5pybcs6tBI6irGX921qbef9r2RBcg/YZmNnlZrbNzLbt2rWr1S6LLiApTSqN9b2E6AYSCfd3zv0U+HvgPcChZuYvCjoKeMV7/TJwNID3+duAPZXtNb5TeYzbnHOrnHOrFi1aFMdpiJyQlCalcHQhOkNsgs3MFpnZod7rucA5wI+AR4Ff8zZbB3zFe73Ve4/3+bdcOWRzK3CRmQ14EZXLgH+Jq98i/ySlSXVrOiYhOk2cGTWPAO72IhgLwJedc181sx8C95rZp4EfAHd4298B3GNmI5Q1tYsAnHM7zezLwA+BSeAK55ymvCIQtcLBxyaSSWybh2SyIjm0dCE6tEBb5JZ6C3gXDPZ1tHhkK8Q96CU5qGYhXVfSNHpW90+WMn1uIUj/Au1OIsEmoHHuR19zy8KAEXeGjSQzeGQlwXLS1HpWN56zjItOX8qGqirXYc4tY0I/Ewu0hegojXxpWVrYG3ewS6LleiI+Vl6WVNR6VlefcAQbtmxv+dy6eR2lBJvILXmJSow72CXJZQlpKWKaNmo9q8OL57V1bnkR+q0gwSZyS16iEuMW0ElOANJSxDRt1HpWR4uTbZ1bXoR+K8jHJnJNxnwMNZGPLR19j5vqZ3Vub6GtIKek6wtGgIJHGiHBJvKGoiKT21+aaOfcfKG/5fEXWX3CEQwvnsdocZKh/h56elJprJNga4QEmxBCwNRUid1j41mJrFRUpBAiOCqf053snyx1ZWSlBJsQOSerg5MIT/UEpt0AkqxGVkqwCZFzsjo4ZYE0acK1JjD7DnRnZKUEm0gdaRos8kBWB6e0E4cm3M6zX2sCc9c/vcDNa1e2vOQlq8spJNhEqpDZLHqyOjilnag14Xaf/VoTmFu+NTKdiPvZ6y/g9nWrQgWOZHUtqASbSBUym0VP0MGpnrYgDbo2rWrC9a5nu89+vQnM/olSy+njKitUtCIYO0XDVXpmtrHR5865G6Ptjuh2ZDaLniDlc/JUCSEpfEGyaP4AV5w1zPDieby0Z4wDE1MM9tceWhstKG/32fcnMNX7ble78vOqAmld2D2LZhrbfO9vFfD/AEd6f78DvDPeroluZKxYx2xW7KzGlnWtpVnS50bagjTo2gz29fC5i0/h4+evYNPWnay47kGueeApRhs8H42uZ7sm46xqV3HQULA55/7AOfcHwNuBU5xzv+ec+z3gVOCoJDoougNfcBQM/uRDJ84wm/3Jh06k0EGjeTf4/eppC0MDvdKg61AoGD2FAlfdt6NKUG2vK/jrXee5fQUKBre0Eejh9ykrVSviJOhwsRQYr3g/DhwTeW9EV1IpOAb6erjhoWfYtOZ4nvn0BWxaczw3PPQMczrorO4GraWettBuIt68MzgQznxY6zqvP3uY3aPjfOSubVz/tR/xR7/6rrLGdclMjSvrVoMkCSrY7gH+xcw2mdkngceBzfF1S3QTlYJj5PV9vPazIqtveox3/Jevs/qmx3jtZ8WODqTd4PdrFGCSxai4pAhrPqx1PS8949jp7CB/u/0V3nfD3/Mbtz8OxrRQm5oq8cZoMddWgygJnCvSzE4BzvTePuac+0FsvWoT5YrMFiXnWH7tg0yWHGtOWsLHzlvB1ffvSE2wQgazpLdEvZyAeU4y3C6tVBeYdT37e2Y8/34gyn7vMyg/gx+954m8P4ORPVRhrsgg8DPn3OfNbJGZHeuceyGqjojuxZ/1fuf53Wx98hUA/uhX38XShYOpGEjjijZLG/Wi37IYFZcUQSJOa31nsO+gUPPNvYvmD9Sc1M3t65GvMySBnlLP/LgKWAF8HugDvgCcEV/XRLdQLTh27S0yNNALLh0DaSuDl+gewgr+ai1v/dnD3HzRSsbGp7j6/h3TWpnvy/3iZafz3Gv7pid/Pr7JM+xvpBs08KBX5FeAk4HvAzjnXjGz+bH1SnQVWRAc0lpEVFT6lAFufOQ5AH73l5bV1MrGilM89PSrfOaDJ87Q5m5euzK01SBPhVkbETR4ZNyVnXEOwMyG4uuS6EYUpiy6hXqpr+qt4SwUYO3pP8ff/uDl6Wjhv/zwqSwcDC+MuiHCF4ILti+b2V8Ch5rZZcAjwO3xdUsIIfJJvUjKQoGaEahzentYONTPb515HMsOn8f+ibL5sZUq2N0Q4QsBTZHOuRvM7FzgZ5T9bL/vnHs41p4JIVJNN/hq4qBeMNKc3h7m9PbUNclHYQqvDNTyadVXl2aahvubWQ/wkHPunGS61D4K9xciXrrFVxMXnZoUpPy+RdaBQOvYzGwr8GHn3FtRHThOJNiEiJcga/vyoNHl4RyqSfE5Jb6O7QDwlJk9DIz6jc659VF1RAiRHZr5alKuGQQiD+dQi26I8A3qffwa8F+Bx4AnKv6EEF1Is1RSeYi+y8M5dCtBg0fuNrO5wFLn3DMx90kIkXKaZWPJQ/RdHs6hWwmaeeTfAzcA/cCxZrYS+JRzbk2cnRMCUu0T6FqaLarPQ/RdHs6hWwlqitwEnAb8FMA5tx04NqY+iZzSStmNbqiFllUaLarPQ1WAPJxDtxI0KvJx59zpZvYD59zJXtsO59yJsfewBRQVmT5adcSnLbN+1rXHJPsf9lhpvLZp7FOSJHz+ke04qMb2tJn9R6DHzJaZ2Z8B/xxVJ0T+adURnyY/R9a1x6T7HyZNWlqvbdpSvSVZbDSt9yQIQQXb/wscDxSBvwbeAjbE1SmRP1oVUGELOcZJ1qPk0tz/NPctLSQtaLJ8T4IKtl92zl3rnHu393cdoMAREZhWBVSa/Bxp0h5bIc39T3Pf0kLSgibL9ySoYLsmYNs0Zna0mT1qZj8ys51mtsFr32Rm/2pm272/91d85xozGzGzZ8xsdUX7+V7biJl9ImCfRYpoVUBVRt89e/0F3L5uVdsLZFs156RJe2yFNPe/sm9rTlrCQ1f+As98+gJGYza3ZYmkBU2z5yVJs2hYGgaPmNkFwPuB/wB8qeKjQ4B3OudOa/DdI4AjnHPf92q3PQFc6O1rn3Puhqrt3wlsoRx9uYRyBYHl3sfPAucCLwPfA9Y6535Y79gKHkkn1Y7oub0F9k+WEnXMt5NNIuuZKNLcf79vWx5/kQtPPmpWFek09LHTJB1I1eh5AeJ4lpLJFWlmJwErgU8Bv1/x0V7gUefcm4EPZPYV4M8pV92uJdiuAXDO/ZH3/iHKywwANjnnVtfarhYSbOmn1o/mcxefQk+hwOBAfIKu3cEh61Fyrfa/3veivB6lkmN0fJLLNz+RmijYNNGJiUm9+xuTkE0mV6Rz7kngSTP7a+fcBICZHQYcHVKoHUO5AvfjlAXb75rZJcA24Pe8fR0JfLfiay97bQAvVbWfHvTYIp1UVxFeNH+AvcVJrrqv9kw9qgG0XXNO1vPstdL/egPqgsE+9oxNRDbQFgrG0EBvZv06cdOJSvP1npe0+9+C+tgeNrNDzGwB8CTweTO7McgXzWwecD9wpXPuZ8BngXdQ1gRfBf67v2mNr7sG7dXHudzMtpnZtl27dgXpmugg1T+MK84a5qr7dtR0jEcZDZZmP1NaaRS0EHUwg+5PY1pdfhDUHxZ0u7Tfp6CC7W2eUPpV4PPOuVOBpvXZzKyPslD7onPuAQDn3GvOuSnnXIlyFW7fT/cycHTF148CXmnQPgPn3G3OuVXOuVWLFi0KeFqiU1T/MIYXz6s7A4xyAE1TlGVWqDc7j0O70v0JRpjAjaATwzATyLTfp6C2lF4vGOQ/ANcG+YKZGXAH8CPn3I0V7Uc451713v4K8LT3eivw154muARYBvwLZY1tmZkdC/wrcBHwHwP2W6SU6iS6L+0Zq5uXL0qzRyfMOVmnXs7E0eJk5LkU83R/4vLHhvW1VZv9/YlhtT8s6HaQ/vsUVGP7FPAQMOKc+56ZHQc81+Q7ZwAfBs6uCu3/b2b2lJntAM4C/jOAc24n8GXgh8DfAVd4mt0k8Lve8X8EfNnbVmSY6jD+xYcMcMvalTVngFGbPeI25+SNRrPzOGbtacv20QpxLqYOa8EIOjEMO4FM830KlCsyaygqMps0irzrdJh6GvrQSZKIimx0nCwRd3RnyTmWX/sgkxVCsrdgPHv9BRRs9rUKGsGYgrysiYX7f9w599+83JCzNkxrBW0JtvzR6QEvBT/63JOHyYN/DguG+llxXXDhE4awz2LQ65qC659MuD9l0x+Uw/KFaJl2BVOnw+zTHt6cB8L4eNKKfw6b1hwfWy23ZkVeqwnqD0u73ywMzdax/U/v/93JdEfkkRTMBNtGRSfjJw+TB/8cbn10hM988MRZGVSiiBpsRQAFnRh2egIZFUEraP9PZpsi36Ksyf2lc+5A1B0T+cD3NyQ1E4/LZBl2lizCk4fJg38OW58sr0jatOZ4hhfPY2x8kqH+6AIs8iKA4iJoodGbgUWUczkC/Drwf4C5wCHOuQ/H1sMWkI8tHSThb6h1vLg0w077+Tp9/LjJg2aflXNI6bOUTPDI9EZmjznnfqFWm5ntdM4dH1WHokCCLTra+QH4Tu5Na45n09adsQde5DnAIysDZrukdMANRdrPIcXPUuIVtBeZ2dLpo5dfv917Ox5VZ0Q44l5X1e5anGp/Q9xZCvLgo6lHlos+hiHNa6OCkvZz6IZnKeg09veAfzSz/01Zqh4L/CczGwIUWNIBkph1tRullpS/ofp4WfbR1CPPQlskSzc8S4E0Nufc1ymnuLrS+1vhnPuac27UOXdTnB0UtUli1tXuD6AyM8XXn3qVTVt3smd0PBahVn28NOava4e0J50V2aEbnqWgUZGDwEbg55xzl5nZMjNb4Zz7arzdE/WIe9blmznb0YCSXheTp3U41SgqU0RFNzxLQe0zn6dcAfu93vuXgfsACbYOEbfZbWxiirv+6YVZa3FuXrsy1A8g6bDkvIZBRyG00x7UkCTdfC3yPAH0CfrLf4dz7tfNbC2Ac26/l71fdIi4Z12D/T3c8q0RRnaNTvvGRl7fl4bIqa6lHaGd4ki4xNG1yO8E0CdouP8/A78E/JNz7hQzewewxTl3WpOvdoRuCfePc9aZ59D5bkT38yC6FqkluXB/TzP7HOVSMkeb2ReBbwIfj6oTojXiDCvOcyBGN9INkXBByeO16NaSSvVoOj1xzjkz2wCcB7yHslTd4Jx7I+7Oic7RDXb4biLPSyHCkrdrIdPqbIIu0P4ucJwX4v9VCbXuoJZGqJlhNpEGfpC8XYtuWHAdlqA+th8Cy4EXgVHKWptzzp0Yb/dao1t8bEmjmWG26eZIwGrydC3CFh5NMYmn1LoAeAdwNvDvgX/n/RddhGaG2SbtqZ6SJE/XohsWXIclaOaRF2v9xd05UZ9OmATT6nSXeVR0M1GbVvPwe8qep1R0zCSYRqe7zKMiLrJirowy0Csvv6egpkiRIjplEkzC6R52tijzqIiDditbJE1UptW8/J6ksWWQTpkE414C0MpsMa3mUZFt2q1skVXy8nuSxpZBOuksjtPp3spsUY5zEQfNBvg8+KFqEeb3lOZrIMGWQfK2DsenldliXq+F6CyNBvismSnDEPT3lPZrEGgdW9bohnVsWXFsh6HVHH55vBaiszQyi49NTOU612SQ31NM+TYj+9Fm/y50KXnMzt1qxYI8XgvRHu1Odhr5k/Pih6pHkN9T2q+BRgGRGpSfUkRBVCHr9Qb4NC57SZq0XwP52EQkROVIzlNGCBGeKJ6juEPW5ddN/zXovGgVmScvizpFZ4nqOYrbTCbLQvqvgTQ20TZ5WdQpOktUz1ESS0BkWUj3NZBgE22TdkeyyAZRPUdpN5OJ+JEpUrRN2h3JIhtE9Ryl3Uwm4kcam2gbzZBFFET5HKXZTCbiRwu0RSRokbSIAj1H6aT6vsztLbB/shT1fdICbZEutEhaRIGeo/RRHa26/uxhLjptKRvu3Z7aKGiZIoUQQtSlOlp19QlHsOHe7amOgo5NsJnZ0Wb2qJn9yMx2mtkGr32BmT1sZs95/w/z2s3MbjGzETPbYWanVOxrnbf9c2a2Lq4+CyGEmEl1tOrw4nmpj4KOU2ObBH7POfdvgfcAV5jZO4FPAN90zi0Dvum9B7gAWOb9XQ58FsqCEPgkcDpwGvBJXxgKIYSIl+p1gSOv70t9qajYBJtz7lXn3Pe913uBHwFHAh8A7vY2uxu40Hv9AWCzK/Nd4FAzOwJYDTzsnNvjnHsTeBg4P65+CyGEOEh1tOpDT7/KzRetTHUUdCLeWTM7BjgZeBw43Dn3KpSFn5kt9jY7Enip4msve2312oUQQsRMrXWBc3sLqV4nGLtgM7N5wP3Alc65n5nVPflaH7gG7dXHuZyyCZOlS5e21lkhhBCzqBWtOq+nMON9mog1KtLM+igLtS865x7wml/zTIx4/1/32l8Gjq74+lHAKw3aZ+Ccu805t8o5t2rRokXRnoiIlTSXmBdCZI84oyINuAP4kXPuxoqPtgJ+ZOM64CsV7Zd40ZHvAd7yTJYPAeeZ2WFe0Mh5XpvIAWkvMS/yhyZS+SdOje0M4MPA2Wa23ft7P/DHwLlm9hxwrvce4OvA88AIcDvwnwCcc3uAPwS+5/19ymsTOUCVAUSSaCLVHSillghNs7RHYdIilZxj+bUPMlkxsPQWjGevv4BCfX+sEC2xrzjJZXdvm5Fo+b3HLeT2datS6SvqMiL7wSvziAhF5Yx345e288beIhjsO1A26YSdESdRO0sIH5VY6g4k2DJIqz6CKHwLvulw0fwBNp67gmseeKoswDaXBdjYeDjToioDiCTRRKo7kCkyY1QnJA2agLTV783aj2c6/Nr6M9m0decsk84XLzs9tGlRGd1FUkT1OxCxIFNkt9JqsEVUQRr+jLdevrixYvgZsWpniWriilysXGz87PUXcPu6VRJqOUSCLWO06iMI+716A4tvOnxpz1hNAVYoINOiaIu4Ixc1kco/EmwZo1UfQZjvNRpY/Bnv4kMGuGXt7Hxxc3p7NCMWbaElIKJd5GOLgTh9Rkn42IKGRMs3JuJAS0C6FlXQTitxO6drJSQNIlDCfC+o2VLVjkUc+NaFyomVb13QcyaCIFNkxCRhRmnVRxD0ewqJFp1ES0DC0ckUYWlNT6bpT8RkaQFoPVOiP7BUa50aWEQStGqV6EY6uXwhzUsnpLFFTD1t58DEVKpmNkECRIIGgKR11iayiyIXg9HJQJs0B/lIsEVMLTPK5y4+hVEvICMtiVebPZRBBxYllRWic3TSQpRm65QEW8TU0nZ6CgXWb9meyMwmqPYU1UPZ7qxN2p4QrdNJf3iaffESbDFQre0MDiQzswmjPTV6KMMIm3YEpLQ9Idqjk4E2aQ7y0Tq2BEiqVEaY49Rz/C4Y7GPP2ERgh3A756YSIkK0TyfXk0Z8bOWKzBJJzWzCaE/1AkT2T5YSy86fZhu9EFmhk4E2aQ3y0bQ4AZIKXw6ysHXGDGui3A98pd3CC5t2zk0LcYUQcSCNLSGSmNk0055q+bT2Hphg92hxuu0nu2snN44jO3+abfRCiOwiH1vOqLZ5z+0tsH+yxNy+AmPjU1y++YkZGtLff+x9XPPAU9Nta05awsfPX8FV9+1IZNGl8k0KITyUK7IbCSIE/MwhY+NTzO0rsHt0nHsf/wkXnnwURx42d5aZ8egFgzPatj75CgWD2y9ZxeBA/eO0K5Aqvw+AU75JIcKiiWFtZIrMCEFD4yu3G3l9lA1btrP6hCO4+v4djLy+b4aZcc1JS9hXnJzVduU5y2cINWBG+P/UVKmtMH2F+YsgaI1jY/Q7qo8EW0bwF0Ivmj/A19afyRd++3RGi5McmJyqud13nt89XeXa/3/royN85oMnTvu0Np67nLv/6YXptgtXls2Q1zzwVE0f3MYvbeeNvUX2T4SLnKx3LmlMxSPSgQbt5uh3VB/ZfjLCYH8Phx8ywMZzV3D1/ZX+r5XM6T1ofqiMavQ1NP//1idfAWDTmuMZXjwPM7jlWyOM7Bpl05rjOfLQuVy2+eC6su88v5s3xya45oGnWDT/4LG/8NuntxWmrzB/0YzKQRuYHrS7aY1jMzOjfkf1kcaWEcbGp7jynOVcff+Oqhna9hkztMqMIr6G9tDTr05rZV9/6lU2bd3JntFxxopT0wJv9U2PMbfGD8X3wV1x1vD0satNmhAulU6aU/GIdNDtg3YQjVW/o/pIsGWEwb4eli4cbPpjrwyh//pTr/K3P3iZS884liMPm8Ntl5w6YzH2YP/McPuX9swO9ffbfHMmMMukGTZMX2H+ohndPmgHMTPqd1QfhftniH0HJmeYCqF2CqowkVKV2x6YmGK0OMn6LdunTZ2fu/gUxqdKjBanZi0L2HjucpYuHGw7KlLRXKKaNNf6SoKScyy/9kEmKzS03oLx7PUXULCD55+z31FkHZdgSwlBHtAof+z1jlerHeDA5GyhF/TYOfvxiYTo5uemS/OoSrA1ImuCLYzAiuLH3qqAbOXY3T7zFqIVuvR3I8HWiKwJtkazs8G+nshnrUnNBkslx+j45KxsJ10w8xSibbpQY1XmkTxRHQG25qQlXHHWMHP7CrwxWmRDC+a/MMeD6CPO/BnngqH+ro5uE6JV/BysoKw8YVFUZAqojABbc9ISPnbeCjZt3TmdOSTqBZhJRJz5UV21lgasP3uYUWWUEELEhARbxLSSBqgybLdyvVhliL1PFNpOEmHCvlY4K9vJOcu46LSlXL75CWWUEELEgvTbCGnV4Vtd06w6c0hU9coqbfZDAz1NEx23g68VVmc7GavyuXVjRgkhRLxIY4uQdnK3+fZ0XyCsOWkJ8wZ6uGXtypY1q0rtcWx8ckbdtY/ctY39E1PTWfWjdkpXLxT3s50MDfTK5yaEiBVNkSMkiqCMwb4ePnfxKewtTvKx+3Zw+CED/NGvvqu8ELpY1raCCKFq7fGRjb84Y4F13JpSvcraqpotRHt0YbRkaKSxRUgUQRmFgtFTKHDVfWU/299uf4X33fD3/Mbtj4MR+AGu1h6r665B/JpSrcraSgMkROuo6kEwJNiaUCqVzXj7DngBIQfqB4RENWgPDkSg+VVpj+0mLq5H2GCZSk2uMm+lZpxCNEelaoIRm2AzszvN7HUze7qibZOZ/auZbff+3l/x2TVmNmJmz5jZ6or28722ETP7RFz9rUWp5Nh7YII9o+NcttmbIW3exu7RYs0BPKpBOwrNr3oftz46wp98qPXExbWoNXvce2Di4CSgjqCrpck1OoaKTQpRpturHgQlTqfGXcCfA5ur2v/UOXdDZYOZvRO4CDgeWAI8YmbLvY9vBc4FXga+Z2ZbnXM/jLHf04xNTE3XI5vpm9pe1zfVyqLKapv53N4Ct6w9mS2Pv8jqE45gePE8RouTzO0NPg/xtUffx7Zrb5H5A72RRkJW18xaNH+AvcVJrrpvRyQLyrs0rZAQdZGPOhixaWzOuceAPU03LPMB4F7nXNE59wIwApzm/Y045553zo0D93rbJsJgf0/svqlaWs+esQkOm9vHRacvZdPWnay47kE+es8T7BmbCKyx1NIe58/pY96cYJpSEKpnj1ecNTztG4zCTCKzixAzkY86GJ3wsf2ume3wTJWHeW1HAi9VbPOy11avfRZmdrmZbTOzbbt27Yqko2PjUzVrlAUxCwY1odUbvPdPTrWddSSMya8Vqs2dUS8ol9lFiJnIRx2MpAXbZ4F3ACuBV4H/7rXXuiuuQfvsRuduc86tcs6tWrRoURR9ZW5vgUMH+2r4plY2nCEFiVzyBV+twfvwQwYysd6revbY6iSgHt1ebFKIWsQ9Yc0DiQo259xrzrkp51wJuJ2yqRHKmtjRFZseBbzSoD12SiXHnrEJNv/zjzHg9ku8GdIlp7JwaKDhw9TMhOYLvju//Tx790/OGryvPGc5P9kdrZCIg+rZ4+JDBtpaUF6NzC5CiFZI1NtoZkc451713v4K4EdMbgX+2sxupBw8sgz4F8oa2zIzOxb4V8oBJv8xib5WCqcbH3kOOFhupdkMqZkJzd/3pjXHc/c/v8BnPngiV99/MOBi6cJBNn5p+6z2ZppiUMIUGW12rpXBMoP9vczp7Zm1KLvVGWW9Rd6aoQohGhGbYDOzLcD7gLeb2cvAJ4H3mdlKyubEHwMfBXDO7TSzLwM/BCaBK5xzU95+fhd4COgB7nTO7Yyrz5W0499pFrnk73t48Tx++VsjjOwanc6lOPL6PsaKk7z2syI3fOOZ6faX9owxFIHZoV6k4YLBPvaMTdSNQAwq9KIutaHSHUKIsKjQaB3aKcbZLEzd3/emNcezaevOWce449JVjBanYglzr3det11yat2CoIN9PQq7F0LEjSpoNyIKwdbuGqpGGo6/7y2Pv8iFJx9VZW4sHwOIJR9cyTmWX/sgkxWBLL0F49nrL6jbPjY+lUjFbSFEV6MK2nHTrn+nkQnN3/dvnXkcc/sK3HbJqQx5mf0rjxGHCa6WmXT92cPsOzBZ13yqsHshRJZQrsgGxBlW6++7p1Bg/py+SI4RZO1crUjDS884lrv+6YUZBUHfe9xCbvaCVRR2L4TIEjJFtsnUVImxiSmGBnoZLU4y2NdDT0/y84VGplNgVsqu/ZOlg5pofw/Lr32Q97/rCK44a3g6iGV48RA9hUJgs6zKaQgh2kA+tkbEIdhqDdrOlQf8Dfdunx7wb6NEWgQAAA9jSURBVL5oJQuH+hMXbvWCQoIEogQJlGkmtKqF3/qzh7n0jGOZN2e2iVUIIWoQ2QAhU2QA6mUSGZuYYsO9M9Nebbh3e0dyGdbzg5VKNM23GGQhdDOzbOW6v/e/6wguPPkoPnrPE6oZJYRIHAm2ANTLJFIv7dVQByIFq/1ga05awiMbfzFQbbco8s9VCtYrzhrm6vujS4YshBBhkGALQD1taLQ4Ox3Wu49ZwGhxckZbEjXFKrWuC1cu4ePnr+CaB57iudeCFRhtN1CmUrBGnQxZCCHCIMEWgHpRgX0F4+aLZuZGvPmimWmvGiVEjlLgVWpdn77wXdPlY259dGRWtGMc+RYrBWtc1bqFECIICh4JQKOoQOdcw6jIdoI6Wu5v1SLsNSct4Yqzhll2+LxYAzn8AJO5fYVyUM2W7YktPBdCZB5FRTYiqajIQBlI6mT62P7753HZ5niyebSTDiwqal0vQKm5hBD1UFRk0lT6oAb7ehibmApkQqxnxgwS1NEqaSj3Ustnp4rYQogkUEqtkITNIekLmertx4q1KwAcmJii5GjLVJfWci9KzSWESAJpbCEJq3XUC6Uf7J+tVX3u4lMY9cyI7a7/SmOVXaXmEkIkgXxsIWmUHb9g4YRHtR8KR2x+tzTQbsUEIUSuUXb/TtGsiGgYqisAlJzLtakurSZSIUS+kCmyDvXWmMUZmJFFU13YtXhpNJEKIfKFTJE1aGYyK5UcByanKJVgcKCHsWLZnNj2+rOMmeqy1l8hRKrROrZGtCvYmq0Di3NAz1LplzSslxNC5AatY4uTZmHpca7HqmeqSyLfZFgUvi+ESCMSbDVo5utKekCfmirxxmgxkmUAUZJFn6AQIv9IsNWgWYBIkgN6qeQYHZ9iw5btdTXETmlzachwIoQQ1cjHVodGvq4kgyb2FSeZ29fDiutqr53DdTb/YpZ8gkKIVKN1bHFTvcas+rOk1mMN9vdM11SrtXYODlbIBqa1uaQCOBpdJyGE6AQyRbZIoeAlQx73tJWJqVhMgGPjUzz09KuzaqrdvLZc900BHEIIMRNNsVukFXNkK2a7wb4e1p7+c2x5/EU2rTme4cXzGC1OMuStm9vnVfGOIhOKEELkAWlsLRI25L9RJe1G+GbP3zrzOJYdPo/9E2WB5RczVQCHEELMRFP6FglrAqwUhBDOF9bMjzU00MPtl6yKNAuKEEJkFWlsLRI25D8OX5ivBX7krm2s/NQ3+I3bH2e/inYKIbocCbYWCWsCjGPtmypSCyHEbGSKbJGwIf/1Kmm34wtTRKQQQsxGgq0NwqzhimPtW5S14YQQIghZSMogU2SCRF2LTBGRQogkaTW6O2mUUivjZGH2JITIBzGXqlLZmjTRyZIyqkgthEiKrPj1JdjaJCuquRBCtEtWSlXFJtjM7E4ze93Mnq5oW2BmD5vZc97/w7x2M7NbzGzEzHaY2SkV31nnbf+cma2Lq7+topB7IUS3kBW/fpyhc3cBfw5srmj7BPBN59wfm9knvPdXAxcAy7y/04HPAqeb2QLgk8AqwAFPmNlW59ybMfY7FFlRzYUQol2SrGzSDrFpbM65x4A9Vc0fAO72Xt8NXFjRvtmV+S5wqJkdAawGHnbO7fGE2cPA+XH1uRWyopoLIUQUZMGvn7SP7XDn3KsA3v/FXvuRwEsV273stdVrn4WZXW5m28xs265duyLveD3CquadDDQRQohuIC2reGuJfNegfXajc7cBt0E53D+6rjUmjGruB5psefxFVp9wBMOL57HPK0HjZ+sXQgjRHkmPpq95Jka8/6977S8DR1dsdxTwSoP2VBG06OjYxBRbHn+RC08+ik1bd7Liugf56D1PsHtMUZRCCBEVSQu2rYAf2bgO+EpF+yVedOR7gLc8U+VDwHlmdpgXQXme15Yqgob8D/b3sPqEI7j6/h0zoig3bNmuKEohRCjk1qhPnOH+W4DvACvM7GUz+wjwx8C5ZvYccK73HuDrwPPACHA78J8AnHN7gD8Evuf9fcprSxVBQ/7HxqcYXjxPUZRCiLbQ+tnGxOZjc86trfPRL9XY1gFX1NnPncCdEXYtcoKG/A/29bCvOBlL4mKl1hKie2incHE3oIiFCAga8l8oGEP9Pdy8dmXNKMpWTQuavQnRXWj9bGMk2EJQT/CECfnv6Snw9qEBbl+3imevv4Db161i4VA/QMvCSdlPRFaQXygatH62McruHxBfK6ouFLpwqJ9CwSiVHAcmpyiVYHCgh7HiFHP7CuyfLAUyD7aTNbvkHMuvfZDJikGit2A8e/0FFEzmSJEOmv2GRHA6dS1jdnkou3/SBNGKRotTXLa5rHHd+Y/Ph9LA2jEtaPYmsoAsC9FRuX620vITt1DListDgi0gzQRP9Y929QlHsOHe7YF/xO0Ip6wkJhXdjfxC0ZJ0aqux8exMTCTYAtJM8FT/aMOG9bcjnDoxexMiLLIsZJdSyTE4kJ2JieJCA+ILnmqbti94/B+t7yMbeX0f7z5mAYvmD3DFWcMML57HS3vGODAxxWD/7MvebtZsf/YGKNxXpJJmvyGRXsYmpnhjbzGWpUpxoOCREDRynFY7c9efPcwlP38M+4qTXHXfjoof8koWDg1ImxJdidZbZpOSc2z80nY2nruCq++PbTyLLgpFgi06qn+0OMdlm59oKdJRCCHSgh+1XW2BWnzIQE0LVIsoKjKNVDtzBwd6M2OTFkKIevhm5F17i/zyLd/m4r96nKGBXub0pnMsk9oQI9V+N0ivTVoIIeqRlcrZPtLYYkRh+EKIvJCFytk+UhtCEsb5nbVZjhBC5AEJthC0ksZGYfhCCJEsMkWGQCmBhBAi/UiwhUApgYQQIv1IsIVAKYGEECL9SLCFQFGOQgiRfhTNEAJFOQohRPqRYAuJohyFECLdyBQphBAiV0iw1aBUcuwrTlJy3v8UVogVQghRG9nSqmhlEbYQQoj0II2tiiyVPxdCCDEbCbYKslb+XAghxGwk2CoYm5jiJ7vHtAhbCCEyjARbBYP9Pdz0yLN85oMnVi3CXqlF2EIIkREUPFLB2PgUr/2syA3feIZNa46fLn8+lPLaQ0IIIQ4ija2CrJU/F0IIMRtpbBUoZZYQQmQfCbYqlDJLCCGyjUyRQgghcoUEmxBCiFwhwSaEECJXSLAJIYTIFRJsQgghckVHBJuZ/djMnjKz7Wa2zWtbYGYPm9lz3v/DvHYzs1vMbMTMdpjZKZ3osxBCiGzQSY3tLOfcSufcKu/9J4BvOueWAd/03gNcACzz/i4HPpt4T4UQQmSGNJkiPwDc7b2+G7iwon2zK/Nd4FAzO6ITHRRCCJF+OiXYHPANM3vCzC732g53zr0K4P1f7LUfCbxU8d2XvTYhhBBiFp1KrXGGc+4VM1sMPGxm/6vBtrXyWblZG5UF5OUAS5cujaaXQgghMkdHNDbn3Cve/9eB/wGcBrzmmxi9/697m78MHF3x9aOAV2rs8zbn3Crn3KpFixbF2X0hhBApJnHBZmZDZjbffw2cBzwNbAXWeZutA77ivd4KXOJFR74HeMs3WQohhBDVmHOzrHrxHtDsOMpaGpRNoX/tnLvezBYCXwaWAj8BPuSc22NmBvw5cD4wBvymc25bk2PsAl5ss6tvD7n9fGBvm8dMar/aZ/Rkpa/dvM+49tvN+6zc7xtt7ucN59z5EfQneR+bc+554KQa7buBX6rR7oArQh6jbVukv74uBIuAF9o9bkL71T6jJyt97eZ9xrXfbt7n9H4rlm51nDSF+wshhBBtI8EmhBAiV6iSZn1uC7n9mcC3Y+hHHPvVPqMnK33t5n3Gtd9u3mec+22ZxINHhBBCiDiRKVIIIUSuyIwp0syOBr4ErATmVH+cfI+EEELEiGP22H6Tc+4/N/tiZkyRXjaSd3lv5wK3An3AkPcfyhpooeJ1rQsjhBAiXkqUx17j4Dg8yUFlasJrG6c8nk952xWAHu/1GPBD4ATgZ0A/sNc593PNDp4ZwVaNmX2F8vqJkyubKZ+8/1qCTQghkmeC8vjby8GxeJLy+OwoC6p5QNHb1m8f4KBSMgH8H2An5bJl/cDbgHc2yz6VSR+bmR0DrAKOoXwh/L9+Ds4SQEJNCCE6QR/l8XiKg2Nyf8Xnb6MsfwaBQ4A3vdc9zFRKfo5y4o7PA/8XsJ8A1V0yJ9jMbB7llFxF4ADlC1D5J4QQIh30cVBIOWCf1z5ZsU2JstCa8t4f8Nr2ed/ZA/wXwNfSmo7zmQkeATCzPuAByjbZca95kpkami+sXUWbEEKIzlA5Ps/zXlfLnv2U4yX8cbsPWAD8FHiUsiKzxtvHrOou1WRGsHnJkO+gXLamVPHRXsqCDsrRktWam39RXdXrys+EEELEi6+E9FAWVAOU/WgFykLtp97rUcoaHJR9bP8WWOi9Hw1S3SUzwSNm9n+TstXtQgghYsGPqvT/F7zXY8BblANIflbvy5kRbEIIIUQQMhc8IoQQQjRCgk0IIUSukGATQgiRKyTYhBBC5AoJNiGEELkiM+vYhMgbZraJcnaFN4BvOOeaLjyt+O4xwFedcyfE0jkhMow0NiE6z6XAkk53Qoi8IMEmRIKY2bVm9oyZPQKs8JpXAV80s+1mNtfMTjWzfzCzJ8zsIa9kE177k2b2HeCKin0eY2bfNrPve38/77XfY2YfqNjui2a2JrmzFaIzSLAJkRBmdipwEeVSS78KvNv7aBvwG865lZTTDv0Z8GvOuVOBO4Hrve0+D6x3zr23atevA+c6504Bfh24xWv/K+A3vWO/Dfh54OsxnJoQqUI+NiGS40zgfzjnxgDMbGuNbVZQLqz4cDk9Kj3Aq55gOtQ59w/edvcAF3iv+4A/N7OVlDOkLwdwzv2Dmd1qZospC9L7nXOVWdWFyCUSbEIkS7McdgbsrNbKzOzQBt/9z8BrwEmUrTAHKj67B/gNyprib7XSYSGyhkyRQiTHY8CveH60+cC/99r3AvO9188Ai8zsvVAu1WRmxzvnfgq85SUDh7Kw8nkb8KpzrgR8mLKW53MXcCWAc25nDOckROqQYBMiIZxz3we+BGwH7udgtYq7gM+Z2XbKQunXgM+Y2ZPetj/vbfebwK1e8Mj+il3/BbDOzL5L2Qw5WnHM14AfUfbPCdEVKLu/EDnGzAaBp4BTnHNvdbo/QiSBNDYhcoqZnQP8L+DPJNRENyGNTQghRK6QxiaEECJXSLAJIYTIFRJsQgghcoUEmxBCiFwhwSaEECJXSLAJIYTIFf8/wr0VSJK4mjwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88e9497d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88f0676198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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FZLft2HoR+Y2I7DT/XWz7v5tEZEBEnhORZbbjF5nHBkTkU3HJS/RBx84HgLfrMC+8Ualh7aaddS6118pjWNNwLAtutrDdKvKecJN14rS07gHwDwDuazj+d0qpL9gPiMjbAFwBYCGAmQAeFZH55n9/BcAFAPYB2CYiW5RSv4hRbkJcyXvPQCeX2ol9paZlADpaJWGt9qAJN7pef16JzdJSSv0UwGDTFxpcAmCzUmpEKfUrAAMAzjL/DSil9iqlRgFsNl9LCIkBp2SMlwbLmS0DCGO1B4mF6X79eSSN7MHrReRp0314nHnseAAv2V6zzzzmdpwQEgNOLrXjSp3YsHKxc11bDjP0gmRR5vH6dSdp/8ZXAfwVAGX+/CKAj8K555SCs1J1XMKIyGoAqwFg1qxZUchKSNvh5lID4FzXlsMMvSAJN3m8ft1JVGkppV62fheRjQC+a/65D8CJtpeeAGC/+bvb8cZz3wngTsBomBuRyIS0HW5xu3YpAwgSC8vj9etOou5BEZlh+/P9AKzMwi0ArhCRbhGZDWAegJ8D2AZgnojMFpEuGMkaW5KUmehBVtvxZFVuv+R1Pym/sbC8Xr/OxLYUEJFNAM4F8GYR2QfgMwDOFZHFMFx8LwC4FgCUUs+IyDcB/AJABcB1SqmqeZ7rAWwFUARwt1LqmbhkJnqS1XY8WZU7CDrX1SVBu19/GnA/LaI9Xvtf6eyCyarchKQE99Mi+SCrwe6syk2IzlBpEe3JaiPXrMqdNfIeNyT1UGkRX6Q5MWQ12J1VubMEi3vbD8a0SFN0SCjIaqucrMqdFRg3zBWMaZFo0KHqX9cmus3IqtxxE5Xlzrhh+0GlRZrCiYFESZQuPcYN2w8qLdKULE8MDNLrR5SWO+OG7QedvqQpWd38UIdYHJlIlJY7i3vbDyot0pSsTgxB90UiyRB1v76873FG6qF7kPgiiwkFjMXpCV16pBW4LCG5hR249SSrljvRA1paJLdwRa8vWbTciR5wuUlyC1f07QELuNsLT6UlIpd5/b9S6oFoxSEkWhikzzfMEG0/mj3F/4/H/ykAVFqEkNSsHWaIth+e36pS6iNJCUIIySZpWjvMEG0/fCdiiMj7ROQTIvLn1r84BSOEZIM0e1NmuVsLCYcvpSUitwP4HwD+F4xOvJcDOClGuQghGSFNa4cZou2HX6fvHyilThORp5VSfyEiXwTjWYQQpFsPxwzR9sOve/AN82dZRGYCGAMwOx6RCCFZIm1rhzVf7YXfZdB3ReRNAD4P4EkYmYP/GJtUhBDtcMsQpLVDkiTwzsUi0g1gklLqd/GI1DrcuZiQaGE9FEkAXzeSL0tLRK5yOAal1H1BpSKEZA/WQxFd8Hu3nWn7fRKA98JwE1JpEdIGsB4qGdiSqjm+lJZS6n/Z/xaR3wNwfywSEUK0gx3z44cuWH+E7fJeBjAvSkEIIfqSdoZgO5BmkXaW8BvT+v9gZAwChqJ7G4BvxiUUIUQvwmYI0t3lnzAu2HYcX792/Rdsv1cA/FoptS8GeQghDegyMQXtmE93VzCCumDbdXz9uge3A/iZUupfAbwC4HQR6YxPLEIIcHRiuube7Zh/88O45t7tODQ8ilotWKlKGrTi7qrVFIZGKqgp82cGrrdVgrpg29Wd6NfS+imAd4rIcQB+CEOJ/Q8AH4pLMEJItlPNw2Yctq0FEdAF264ZnX4tLVFKlQFcBuDvlVLvhxHXIoTESJYnprAd2NvVggCCtaRq1w73vpWWiJwNw7L6nnlM72UeITkgyxNT2IzDLCvqJGnXjE6/imctgJsA/LNS6hkRmQPgx/GJRQgBjk5Mja6yLExMYTMOWRPmj3bt+Ri492AWYO9Bkid0yR5MinaNaRF/vQd9KS0RmQrgEwAWwmjjBABQSp0XVro4odIiJNu0m6ImAHwqLb8xra8D+C8Ye2j9BYAXAGwLJRYhaM+UZuIf7pFF3PCrtKYope4CMKaU+lel1EcBvD1GuUiOyXLtESEkXfwqrTHz5wEReZ+ILAFwQkwykZzTzinNhJDW8JuKc4vZ2f3PAPw9gGMB3BCbVCTXMKWZEBIWv5bW5TCSNnYrpd4D4AIA749PLJJnslx7REirMJ7bGn6V1mlKqd9afyilBgEsiUckknfatSiSEMZzW8eve7AgIscppV4DABHpC/BeQupo16JIQrLcS1IX/I7SFwH8h4h8G8a+Wn8I4NbYpCK5J+g2F81gXQ/JAoznto4v96BS6j4AHwDwMoytSS5TSt0fp2CE+IUuF5IVGM9tHb8xLSilfqGU+gel1N8rpX4Rp1CEBIEp9CQrMJ7bOnSiksxDlwvJCoznto5vS4sQXaHLhWQJtqhqDSotknnociHtSjvWfNE9SDIPXS6kHWnXLVxoaZFcQJcLaTfaNQGJSosQQjJIuyYgUWkRQkgGadcEJCotQgjJIO2agMREDEJIaNg+Kz3aNQGJlhbxTTum1+aVKL5Lts9Kn3ZMQKLSIr7gBJUfovou2zV7jaQLlRbxBScofWjVSorqu2zX7DWSLlRaxBecoPQgCisp6HfppiTbNXuNpAuVFvEFJyg9iMJKCvJdeinJds1eI+kiSuUvJtHf36+2b9+ethi5ol1bxuhGTSnMv/lhVGyWVUdBsOfW5SiIv+8hyHc5NFLBNfduH99pFwDOnjNlfKddZg+SCPF148SW8i4idwP47wAOKqVOMY/1AfgGgJMBvADgD5VSr4mIALgNwMUAygCuVko9ab5nFYBPm6e9RSl1b1wyE3faNb1WNywrya5ELCvJ7w7QQb7LZq7EqHegJiwjaEac7sF7AFzUcOxTAH6olJoH4Ifm3wCwHMA8899qAF8FxpXcZwAsBXAWgM+IyHExykw8aMf0Wt2IyiXn97ukWzhZmKXbnFjdgyJyMoDv2iyt5wCcq5Q6ICIzAPxEKbVARO4wf99kf531Tyl1rXm87nVu0D1I8kyrK/Eg76dbOFmauWNzTrruQRemK6UOAICpuKaZx48H8JLtdfvMY27HJyAiq2FYaZg1a1bEYrcXQSdFujOSpRWXXFAlRLdwsjBLtzm6ZA86PQHK4/jEg0rdqZTqV0r1T506NVLh2omg7gm6M7JFmOxDuoWTg+7Y5iSttF423YIwfx40j+8DcKLtdScA2O9xnMRE0EmNRcfRkUSbLK7k9YZlBM1J2j24BcAqAH9t/vwX2/HrRWQzjKSL35nuw60APmtLvrgQwE0Jy9xWBJ3UOAlGQ1KxoyiyD0l80B3bnNgsLRHZBOAxAAtEZJ+IfAyGsrpARJ4HcIH5NwA8BGAvgAEAGwH8KQAopQYB/BWAbea/vzSPkZgI6p6gOyMakrJY01zJ695wOUn5vD6L7lhvWFxM6gi64md2WTREUTTs+7NSSJzR/T5JUr4wn9UmyU6+LohKi0yA2YPJk/dUZ92vL0n5gn6W7go/QnxdjC7Zg0Qjgron6M5onbwH4HWPfSYpX9DPYrJTPekvcQghuQ/A654AkqR8QT9Ld4WfNLS0CNGEPFusuluSScoX9LOY7FQPY1qEmDA254+w46T7+CYpH1tpOcJEDJIuOk9SjbJN7ihgsDwWeGLQ+RrjoI0mUK1ok/uMiRgkPdJu7+RVB+Mk2/Bo8GB32teYBkwKSIc8u46DQqVFmhKm6DLNya2ZMnGSrae7I3CwW9cJPM4iWSspYMWimdh6w7vwy89ejPUrFmJyJ6cSJ3QvqM4ivNOIJ2GtiWYZT1E9zE7naaZMnGQbODgUONitY1ZX3NZfebSKNefNxY0XLsD6Lc9gwacfxvotz+TewgxDO1riSUClRTwJa014ZTxF9TC7naeZMnGSbevuA7ht5WLHjC43BatjVlfc1l+ps4irz5mNT37n6brPWLtpZ8ufkTerRFdLPOtQaRFPwloTXmm9UT3MbucZHql4KhMn2VYuPQlTSkad1J5bl2Pjqn5M6ekCAFcF6zd1uXEyrlZrsbvv7ERp/RUKgt5JwV2pzcijVaKjJZ4H0q/qI1oTtujSq1jWHhe57j1zMXdaLwYODgWOi3hNChtWLpmQ4WYpEy/ZeouGDNa1DY1UxhUjgHHFaLXcaVYQ3Jhtt+a8ubjirFlYu3mnZ/Zd2GyxJIpk4/gM+wIEmDjOWUT3guqsQkuLeNJK0aVbxlNUcZFG99yKRTPx6Lp3Q0TQ013ExqvqrSb7pO83G6vZarnZeRqtwWWnzMDazTs9rUy71bHuGzvx6uERQIChI82tsriLZGs1hYIAG1xcqWHJo1Wie0F1VqG6J57E0V7Iiotce/8TdSvrtZt2BlpZW5PCmk07MP3Ybty4bAE+/q2n662rrmJLq9pWV8uNk/Hcab1NJ2dL0U09phvrLliAT37naU+rzI7T9zW5oxBJjY/dapx+bDc+d9mpmDWlhPKIce5W7ok8WiV5b82VFrS0SFOirhGJKi5inxRuufRUfPxbT0cW9LbiUJM7C64JGn5otAb9ZClaiu6698ydkPDg55rs31eps4jB8lgksSK71fjgzv049ws/wYc2Pg4ImhZgN4vh5dUqYX1V9GRzCUMyT1Qra2tSqCkVmXvJKQ51x4fPQO+kjsCrZbs1uO2FQSNL8YrFE2Ja9snZGhs/VlkzoowVhXHh+e2gkZRV0iadJXINLS2SClGvrKNMP2+MQ33p0edx7f1PjCvUIJOcfTLec+tyfPSdc+r+doq3WWPz0mC55WuKMlYUZoyDZIrGbZXkMUOxHaHSIqnQOJk7Td5BiFIJRjHR211i5TFzRW9OxsViwXNytsZm2rHdLSc8RKnMG8d43fnzcMeHz0Cpq+ju9tMowSKvdVN5q29rBt2DJDWslTWAloPtUbqXWnVdRtFU1igN6MCkjmJL19TonnRyR/rFPsaTOws4NDyKa+9/wvMadUqw0EmBRkU7NjCmpUVyQ1TupVattihX9K1eUysWba2mUB6tGKn2SmHoSAWAscB4Y6yGtZvcU/ejSmSJEh07mLRKXq1HL2hpEdJAq1abbiv6MBZtraZw+MgYDo9UGsoIFmNKT7fnNUaZyBIlUVqduqDbvZYEtLQIcaAVCyeJFX3ccYzyWBWvlcccygiMHoNe1xgmkSWJuEzUcdSkcWwOnUPrsRlUWiRTRDm5xTVRBnEvhpEhiSy4UlcRJ/aVXFfxXkkZQVb/lgvy0PBIIll9Wa2bcvvOJ3cUclnf5gV3LiaZIcqgc9wBbKd6IACR7JY8NFLBNfdur0tuOHvOlEj79A2NVPDq4RHc9MAu18+xrtFKyli7yag9e3Tdu13fZzVMLnUVcWSsiuGRCoZHqp6fQ7y/c/uYZrz2jDsXk3wRZdA5jgC2V5o7MLFbfJjdkoFk4hilziKOK3Xi85ef1rCKX1zXeNgpKeNLj+xxeN8STO4o1I3BwddHsGbTTk+Ljhh4fedZtR7DwmUMyQxRTtZ+z+W3g0Izy82pM0WY3ZKBozGzqcd0j3fJf2mwjCNjVZS6nB9pr+tw+79jJnWis6OAjVf1o9RddO0x2DiWW57aj4Lg6PvMczaOgaWsrNZWOqTF64pOpQNpQ0uLaI9lwZRHIux64SOAHSR21Opuydb29c/dshzDTWJbpc4ibr/ydHzioqNd8m96YBeGXfbq8rqOarWGV13iSVatWO8kcxU/yXkV7zSWL79udKa3r/4bx8C6/q/8eAB/84GJllme4zJByWtvxjAwpkW0prGzuFMn97hiWkFiRzWlMP/mh1GxKZuOgmDPrctREHE817rz5+GKpbOw+fEXcemSEwJ1cx86UsE19zmfz4otWeeZ3Fmc8Nqz50zBXVf3o1JVdd32va6xlbF0Gs8Vi2biExcZ3+f0Y7txw/nzA3eNb6degm1wrb4upr3sSpIKrTxsjS6lmsLRLTFaeHAba7GOjFVRq8HYt2qkglJn8yw4+3VZuyW7uW+caoRWLj0JfaVOfOQds7H6vvptWtZs2oG7ru5HTcFx3ErdE2VbdsqM8diS/Txfv2ap43XUanB1UU7uLBjj4OM781vX1jgGc6f24LhSV537EQronRStsswLUXaQyTLte+XEkahXc61OLE7xkod2HcCeW5dH0vrJyoIbHqlOkHFSZ8FREQ2PVNDT1TGhgLZZ93ZrY8q51dTBAAAgAElEQVTG+FBPQSYojunHdmN4pII1m5x3OG6McaxYNBPzpjt3hbfcqo3XUeou4vmXJ8aT1pw3ty4b0O8+Xs0mVKc2UH907/YJ+5/5JY+7HZPmMKZFxomj/qfVLL0kiifdZCx1FSfEWv7mA6cZCt2hgHbzz1/EnVedMaFw1RrXj92zHYv/8gf40MbH8Ybt+p2u8Ybz52ONR5ske4zj0sWGm+3FQ85d4QsFOMZDyiNVbN19YMI1Xn3ObM8WTW74qTlzyzgMk73Zjt0gCJUWsRFHGnirE0sSAWg3Gd8YreHBHfuwfsVCPHfLcqxfsRAP7tiHN8Zqju/Z8KMB9DikHjdN0nC4xllTvNPAnTbA/NIjexwTGiZ1FB07QZS6ili59KS6a7TaLYXdN8vvgicKhZNGN4h266iuI7ShyThxrFxbTdVNYnNANxkLBWDl0pMce9UFua5m4+p0jW4uPfv5GzfAtJJA1q9YiLnTevHGaH1Cg5P7bkpPFz76zjnjn9vb3RHqOwvqqmvlvrAXNd+2cvEEN2ZcGXVZi6HlNXGDllbOCbIyjGPlGoWlFEXxpDUO1VoNh4+M1Y2Hm4xuFkqhIIGuy21cj4xVXYuRS13hzr/lqf1Y9uWf4sp/fNxIOW8yVk5jG+Y7C7rgCXtf2C26BZ/+PjY//iLu+PBEl2wceFnMTh3x07TC8rzhJVPec0zQlWFcK8m0V3zWdW16/Ne4dMkJeHDHPiw7ZQbmTus1kyqKEJHAMrZSeHz7ladjtFpzTbTwe/5aTeFIpeqZtBF2zIKMR5jWUnaLqTxaRU938y7wTqn+Vvq+W6ZlVLiVNTx3y0UYOlJx7YifhnWTRKuvGPA1UFRaOaaViURnl0LYCXX9ioXYuvtAXU3UmvPm4upzZse+bUajzFBwnHzD1keFrXOKirALniDvq9UUIJigOC5dPBM3v++trko7qnva7Xm686ozcGhoVKv+ic3qBjWFvQfbnTAxKt37mIVxe1jjMHdaL5adMgOf/I6x3cbFp87ApUtOwLX3PxHIheLmcvVyETWOq1OdVeBEBJu76sGd+3HuF36CD2305xaMmrDbfgRJ/imPVR0zJL0yLaN0kzm5NG+/8nT0dHdo1z8xz1uWUGnlmCzfuG6KIUyGo5XUMHBwCHOnHa1luu49c8cVmN9zuU2C1aoRKxscHsU195n/d992HBoecW77FMF3E3XiTKuZcVY8rGwmgFhKw4sg11DqKuLLj07MkPTKtAybEes0Fo2K+a6r+zFareHFQ2W8NOhcbpDWs5ZU26c0simptHJMVvuVea2Og07UtZpCtVbD5y8/DVt3H8DQkcr45GJXYE7nctx0z2MS9No0sZHGOquf3Hguvn7NUkDB94PfqPhWLJqJR9e9GwACTyBRWCRhzhFEeZdHq3j59RF84QfPjafof+6yU8e7kTidI4xi97oOu8VcU8CaTTvxpUf2oKer6NkRP2mS2PAyrWQPxrRyThZiVI14xeIABIrTWeeyOqK/5c09GHzD6PawfsVCrN/yjOseRY6xlt4u11iBUsCCT/uPI7SaRBFlX8YoAvdhY6hBYlpOr+0rdbruS1Yeq07od7jugvmebcD8Xoc9brRi0Ux88qIF+L3JXZ4d8fNEDMkeTMQg2cKuYN0UAxSCZUQ6BKQvXTwTt1x6KiZ31W9e6DXZAUeD7vY+gfbjYYLxrT741pj5Sezw3J4kgsB92HMEWVi5ZRxO7ijgjUptwjnCKHa/15HRDL3IiCHZg4kY7URQ37Julf12V4PVD8+O5e4J6vbw2jajWCjgzT3dznVYHm4lN5drs00TnWg1LmW5q5oldjRz5UQRY7POYW2z8svPXoxH170bR5rEj4Ik/1hxs8HhMay+72gCzWB5rK7OzTqHU+cQt4QN63nwcjdaYzk0UhkvbtbZ/R7nc55WzJyWVg7QpR6rFeyr1hWLZuLGCxcE2qrDjbDXGmZ7c8vdV6vBt4soqtV6s/M0ukmtjSOnHduNUldHJPdEraZw+MhY7PVKYcfMq85qcPioe3HNeXNxxVmzJjQ/ntLTBaDe0k+qZCIMcT/nMZyf7sF2IehDrKNbo3FCWbFoJq57z1zMm97b8mQQJq6XlGKP6nOanaemFNZ9YyfWXdC4GDiqUKKIf7oV/0Z5b4V1S3nVWTW6fNedPw8fecfsCQXPOj47biQha8Qxc+6n1S4EbqGjYXfsxl50W57aj1cOj+Cuq43kC/s+V2E6VQTdhyiJnodRfk6z85RHq7jh/PnjKf4AxrMbrUksiv2aoqg/a0bYvoX2/bzsxdiWjHY2/GgA17933ri7cfwcGj47biQhaxp7fDGmlQOC+pZ1rN9yK9wcNleLflNqo0zDTarQOqrP8TpPqbPoWc8UVewjiXsrbCmHpdjvurofN7/vrbjpgV2Yf/PDrlu6uKXd6/bsuJElWYNA92AOyENMy5KrlVZHtZrC8GjFMbtPR/dN0nj17XPaBDPJGGJQWnFLNbrNViwy9iPzUy6g67PjRJZkNWFMq50I+hBnoX4rSOzCekD7eroC1Uq1E26T2OTOYqRxKN3vLa8yiFK3/7T7WJvzRvQZun8XDTCm1U4E9S2n4YsOSpDYhdWpYv2KhS3t35Vn3OJekIkxnVZiH7rfW073lVUG0RjDciLu64vSQtL9uwgDY1oZR7d6qygJEruwgs5f+fGA4+69OtXOpIlT3CuvsQ83dG9vFscO4nkiH6q3TcmgzzqQu6JZRpz9XFZB6Jan9gM4untvebSCni79utXrgL27RJI7AKdNUpmhYclShmIaUGllmKBbnKdNGCXr5t5oPNea8+bitisWY+3mnXho1wG8cngkNQWuexzBqefhmvPm4o4Pn6FlkWwc6Ow2C5vS3y4wESPDZG2jtyiLHZ3O5VYQmiS6W7+WfMMjFa02LYySrCcl+b2HdJM7Ath7MO9kLRYRpdvD6VwbfjSAHo96pyTif7rHIyz5dNu0MCqC1umltb2GF376a+ood1JQaWUY3QPKjUSlZGs1Vbcvlp9zJfWQN1PMaSfOWPINHHRvSpxlyqPBFg26LjKaFZzrKncSUGllGHuF/84/vxBfv2YpJmuqsIDolGx5rIp7/v1XE7IEb/PoqO71kEepSLwUsw6rY0u+PGZZ1moqcBuprCY9ZFXuKMi285oAQGTdDOLGK2sriH++1FXEhh8NYOCV4fEswYGDQ57X7PaQT+4sRBqDsve3a8zE0yFxxi7flx55Dp+77FRjQ8QcbFpYHqvi1cMjgZIY/CQ96Bg7audkDSZiZJwsdZ12I2jyQphrDtLhu9Xxc5vkdEmc0XESjgI/newnvKdZd3xNE2t0latF2MapHdBlImyFoEoozAPr+p7erth32w17nSQYzfYMc8Pru9T5O8vh4kPfNk4i8gKAwwCqACpKqX4R6QPwDQAnA3gBwB8qpV4TEQFwG4CLAZQBXK2UejINuXUkD26CoP75MMWhbu8JM35hV7lerkPSOvbxfd+Gn42P76SO5t3f3Wq2dI4d6VxrFidpJmK8Rym1WCllbpiETwH4oVJqHoAfmn8DwHIA88x/qwF8NXFJNSZrGYROhMkqDLOdh9N7woxf2MwtP6nMJDxxjG/Wykr8kHYGa6uk4h40La1+pdSrtmPPAThXKXVARGYA+IlSaoGI3GH+vqnxdW7nbyf3IJB9N0Ha/vnAxag5cMkSf6R9b0ZNlNcTw7yjr3sQgALwAxFRAO5QSt0JYLqliEzFNc187fEAXrK9d595zFVptRtZdxOk3Qsu6PjlwSVLDJpNvGnfm1ETVQZrmso8LffgOUqp02G4/q4TkXd5vNZpBCaYhyKyWkS2i8j2V155JSo5SUIktUtwFOTBJdtu1GoK5dEKho5UUK3VcPjIGKq1Gl4dHmlaN5ele7MZUcXo0ixuTkVpKaX2mz8PAvhnAGcBeNl0C8L8edB8+T4AJ9refgKA/Q7nvFMp1a+U6p86dWqc4hMHsu4nD0K7xKby8p3WagqHj4xhcHgUd//bXvzmtSNYfd8TGDg4jLWbdoaeeMOMT9pjGlWMLs0ElcSVloj0iMgx1u8ALgSwG8AWAKvMl60C8C/m71sAXCUGbwfwO694FomfxgevWq2l3ukhafK0+nZCh+4dUVEeq+K18hg+/q2nseyUGfjkd57GY3sPYe603tATb5jx0WFMI+tKk2KCShqW1nQA/yYiTwH4OYDvKaW+D+CvAVwgIs8DuMD8GwAeArAXwACAjQD+NHmRiYXTgzccsN8b0Z889bYrdRXHGwTbFVUr/RebjY+TRaXDmEblJUjTRZ541FgptRfAIofjhwC81+G4AnBdAqIRHzgFcnu6O7StZSHh0Lk+KSjl0aPtnSxF9djeQ+P9F+u7Z/ibeL3Gx6uQ3e+YxpkRHEXiVpoJKmyYSwLh9LDmtWN4O5PF+iS3eFGps4jjSp34/OWnYevuA+ONgh/adQAP7tiHOz58RmCrw2t83Cwqa3dtp/fY5febIJI2abnIqbRIIJwe1q27D+C2lYuZTZcj4nL/xJWI4BUvKhQEx0zqRF9PFz76jjk4/rhJuPMqQ1F99J1zQk28XuPjZYW5vccuf6sJInGSdiIJwN6DuSYqF4P9PEfG6rdpt1wffaVOvFGp5aKWhRhE7aKKurbHLt/wSCXyxsdBPt8+Pl79Cq1u/17v+eVnL8aCT+tXvJ5AbRZ3Lm5nospUajzPx+7Zjq5iARuvqg/kFouFXGfTtSNRu3/CJiI4re4b78tSV/JxVbfx8bLCXN9js850dbfrkEgCUGnllqhuMKfz/PHXngQEgSezoK4FHVwRJDrCJHe4Lb4adyjWaaJ3ytDrK3UaG4663Mt2t7uuG3TqkpxDpZVTorrBGs+zYtFMrF+xEKWuYiBFEtTy06GmhURLmOQOt8VX4w7Fukz01kJr3NGlDMtrsDzmeS/brbNWEkTiRJfkHCqtDBHE8ojqBrOfZ8WimbjxwgVYv+WZwIokqOWniyuCREeY5A63xVd5pP7+3vLUfjy4Y994gkUaE71fq9DpXm60zsImiMSJLu3LqLQyQlDLI6obzH6e694zd7ybQFBFEtTy08UVQaIjTGGr2+KrUMCE+3vl0pPQ05XeRO/XKgSc72Xdu6zo0r6MLakzQtDuzFEV/zWeJ6wiCdoZnZ3U80nQwla3jTMndRQxqaOoVff1ZlZhHu5lHXaUoKWVEcIojKhWbtZ5WnE5BrX8dHFFkHTxWt3rZpkEsQo3rFyCyR2F8c7zNaWMnwnFbLOc5MQ6rYzgVfuR1Iqn1TqNwJstZnxzS9JeeD0fAHCkUkWtBpS6izgyWsVYtYbDIxV8/Fv2NlKLMaWnO9b7XOONLX19OJVWRkj6RnNTGFQkhLjj9HwAhsKyF+U/uu7dAICbHtiV+EJUhwWwC1rvXEw8cFMMSTWobKYg0/ZpE6Irjc+H9SwNj1TqFNSJfSUASCXZKOtJToxpaUazHmpJ+PCZbk5INFjPkrU1isXAwSG8NFhOpe5Jl3qrsFBpaYYOCiPrKzFCdMF6lho7dnzlxwPonVTE5y9vLIheHHuyUdaTnOjfaSDtmI0OCoPp5qQdSOJZt56lxr27Xjk8gs5CAaVSBzZe1Y9SdxHlEUOWuOebNPfCigImYtjQIatGhyCpfRymH9uNG86fj1lTSok9VITETVLPOp+lQDB7MCi6KYy0swQbM578yJK2pUqIH5J81q1nYnJnAeXRKnrMmkc+GxNg9mBQdHDN6ZQlWFPAGnMzOqB5Fw63VeXQkQpXlUQrknzWCwVBqbPoWcPFhZ5/mIhhQ5esGl2yBP082PbK+uHRCtZs2oGpx3Rj3QULcNMDu4wMyPvYoZ3oRdLPutuzdqRS5W4GAaHSspH1rJqgNFNKzR5st434WmmsS0gSJP2suz1rtRpSzxbOGnQP2tA5qyaOWFGzLMHJHQXctnIx1jbEtKwHu7GJr5XWO3dab+puVkK88PusR/XcuT1rfjvAk6PQ0mpAtyacQHwbInqtNms1hcHyGDY//iLWr1iI525Zjjs+fAb6Sp2OW4QDRzfiS6tokpAgNHvWo3zu3J61xn3BAD4rzWD2YAaIM9PJbSXp5zOdXrPu/Hn4o3fOMeNb/rMOCdGNqJ87t76EaZfZNJMxQTmYPZgX4sx0cusl6OcznfY6Wrn0JEzqLGJSp157HRESlKifO7dnLY6QRJiG1zrUqfqBSisDuPnDj4xVUVOIRTH46YrRLC7gp7Fu1Cs71okRIJr7wOsZKHUWI7vPom5C7aZ8+kqdGCyPuSolK0Y99ZhufG/NOzF3Wi9eGiyjp7uIUlfHhM+wb7WSZLE0Y1oREeemak7+8NuvPB3DpvsiqjiX/RoKAmxYubhpdlUrMcCoY3Vxxf5ItojqPnCLQ03uKGh3nzmVnjhlJDYrcZl+bDduvHAB1m95Bgs+/TBuemAXhhvms1pN4fCRMQwOj+Ka+7bbylpGEhkDxrQiIAmzunHlCAVcc19r/nb7OY+MTex+cfuVp6NYKBgrqRislqhjBjp0NCHpE+V94GSxlceqnudP2nvQOP88d8tyLPj0w6jYFEhHQbDn1uWYf7Pz8YIYcexXD4803ePL7+tC4GuQaGlFQBKd2RstmlZTZRtXowdfHxnvfmFdwx9/7UlAEFsmZdQxAx06mpD0ifI+cPIkeJ0/De9B4/zT2FF+xaKZeHTdu5tmKpY6i5g1pdQ8lt1VnLDVitPr4oJKKwLSmCzDVPR7uRDSuAmj7kqgS0cT4k2crnQg/vvA6/xRL2D9nM+t9OTsOVNw6eKZ+MRFRneaTz+4y2ErlKMu/0JBJig2S+EBGP+uyqPVVMtaqLQiII1aC6vw129Fv1v3CovG1VmYawg6GUXdlaDdOppkkSTijlHdB273s9f54/YerFg0E+tXLESpq1inROzP7pan9uPBHftw51Vn4JZLT8XHv2V0p3lw53787fefw+cuOxV7bl2Ojav6J4QwSl3F8Vi2XeHZv6vJHQUcV+pMZS8wgDGtlrGCkodHKvj4t562xbQWY0pPd6yNbjc9/mssO2UG5k7rxfBIBT1dRRSLzusQu59/xaKZ+MtLFuJPvvbkuE96xSLjBq2/Bv9xubBxPWYPthdJxR1bvQ+aNpNuob4xCI3P7Y0XLhjfk8tPViAEnnEsp+s+fGQMRyo1lLqKWH3fE47XUuosxpE9yK1JksC6qaYe043r3jN3PE102rHdE9JEo6BWM1x7bjeT24NRUwrzb34YF586AzdeuAAP7tiHS5ecUPcABEm8iDIxhIqmfbDuQz+TaJr3RVjlE3VSlv1861csxPotz7gqkSiU6NBIBXf/bC8uXXICjj9usmtCh5PCiwAWFyeBZb5XagpbntoP4OgXGzXWDdzX0+XogpjcWcDQSMXxIbdcCPZmtgOvDGP9ioWYO60X5dEKerqOJlsEfTC/fs3SUG6RrBQ0kmjwuyt22vdFWDdf1P1LG8/nJpOVKAI0NAlwaADg5SotdRWx7JQZ+OR3nsb6FQu13MGcMa0WSTL4bwVlneJPa86b6xkrsG5eezPbLU/tx7Iv/xR/9s2dEAgg8BWLcgoOv3goXGA2icxLog9+401p3xetPNdhahe94sHW+fzI1Hge4GjHDbc4VuN1W3OEPaFDpxgxlVaL+H0Io8iYslZaTjfT1efMxtqGlHX7Q26t2MqjlQnZQTcuW3C0SNBHYNxpxfflR/f4Kkb2cy6mqecXu+XgNYmmfV8kmdTjNzmlmUzWee7+2V48//IQJncayRpKqaZK1JqfJncWMDxizBFbntqPL/zgufGG2XdedYYWHhC6B1vEjzsgKleHtdKy3JB1rr3ujqYPeaEg6OnqqHMXrLtg/nh2EdB8d2K7HHa3wcuvj6CnuwMbV/UH2lbcj7uIMa984adtkV83YpwyJrVNUeMWP27PYDOZymNVbHr81xNi1betXIw3eySFVas1HCqPYvPjL2LZKTPwljf3jG9J9NCuA3jl8IhWLnsmYiRAVBlFXsqvWZV+43ksJQAEyy6yy+GUvQgAh8qjE/bg6it14g0zI+nI2NGsI6dOHI1ZWox5tR/t9L0HSU5pdp7nXx5yTdZwmmssC+uef/9VnbJbc95cXH3ObPROar7wjBBmD+pCVDcl4N29OcxDHlahWquztQ1tn0QE195fn9m47vx5uGLpLKzdtNPobbasPrXeK2uRrZnal3axsKO6xw33XjFQxp/1noGD7souyubATWAbJ12IMlnDLcjrN1bQSFjf/RuV2oQY2mvlMUc35bJTZoy/9k/OnTvujvTTLirt2AZJj0JBjAlz1Jwwx6q5bH4cVfys1Fkcj0fZ8ZprSl2GwnLbbXxyZ8ExTlat1oJdZIRwqeqTsKu+Wu1ox/RGF1iQm9LP54fZ4iCs795JmZzYVxrPbLSv2OwPhNvD4aaE0o5tkPRoFxeh2zMIwLWExe08PV3F8XhU41zTOIdM7ihgeLSKrbsP4L/9wWzX5yxMnCxOaGn5IGzrGet9H7tnO2793rNH26dcZVhBAHxlFDZ+/t0/2+v6PrcsRT8ptUFSdJ2sx5cGy9i6+8CEzEb76i9ouyi2Zmpf0k57T5LGZxBAKAunWCzgzT3dEzwu1vnsc9jwaHU8lvUfv3wFX75iYvZvT3fHeN2W/XtYu2lnat8DY1oORNXtwctXXeos+l5F+mnlYr8xg27+FnaMGj/r9itPx2i1hk1mFpKVoFHqLOK1N4zPd4ppNZOlXWIbpJ4oY8FZw96ZIgoLp3EuWrFoJr58xeLxLjnXvWcu3jK1B0MjFRw7ubNuG5agcbIWYCJGGNy6PYR5eLweuvJI1bcitJ9n6w3vcg2YAnBUkndedcZ426cVi2aOt5tq7IIRZqwalQmAptt827MHqYSIG+2chBMmE7DZ+exz0dYb3oXujkLTPbEsD01jclVM3wMTMcLQuOX01/5oKV5/Y8yXS6vRBefW/f3IWLXpflh124jY3GteMSF7nGnFopnYesO78LU/WjqeHGFZadaupKvve6KlDttObkWvRBHreKmrA72Twu12TNqHoDsZ5Al7Zwo7YZKQajWFoSP1CRpzp/Xiy4/uceh4Ud+p3R4n0+V7oNJqwL7l9NbdBzBwcAi93R1NvzSnuFe1VnPsElGrwbPtUeO5/unffoXbTH+zV0zIijM1Kifrs+x9B/MeIyDZplZTGCyPYfPjL453ZLjjw2egr9TZFoucZpmAQTrslMeM2JVdQb00WMbLr4/Udbz43GWnosdhEekWJ0vre6B7sAFrK+ktO38TqNjOzZVx19X9qCnUu9AEWPeNnVh3QWNsajH6SkahcGMX93Xnz8NH3jEbpS4jFtaYHWSPaVWqNaz75lMTth2Z8XuJd20mJBTt7Bq0cKqFDBOjtu/wYIUG/u/v3kBXR8G1qB/wrgmNKcbMLu9hsLactmfMAMCXHn0ej+0ddH1onFLApx/b7Ri7GRqp1K1yrO1MjunuwGB5zLGL+4YfDeD6985DQWR81eN00/SVOiEFqXv/lqf2oyDAX116CtPHSSZgfV69hWN/1t3aPjktkAsFqWv/ZrWAsxbUbvOIW7lBHEldQaF7sAFry2m7P9keH4KCoynemAJub0S77hs78erhEaOL+pEKJncUsGHlErxyeATv2/AzXPmPj6OnuwOVmnLt4m6PoXmlqL9RqTm6Hl9+fQTFgjB9nGSCJHdP0BmnZ91tgTxsWqeNZTluZSOTOoqu84hXuUHaZQh0Dzpgz5hZtnA6lp86AzdsdjejrfdYK5OLTpmO9y85Adfe/wSmHtPtutuo1YvPKvSTgtRt1OiU1t5sNVNTaoLr0e7aZOYeyQLtUlgcBifX6U9uPNczEzCoS88r8znGMgS6B8NiZcx81eyl98e2dM9mHZj/6ep+vH6kgl4zY+97a95Z52Z0en+ps4jh0QoODY26d3H3mZpeHqnWuR7fMrUHg8OjuPb+J+or5LuKdAkSbUmyy3rWcNrYcdaUkqc7NWi3HLdONFZySJohBroHXSgWC+goFMaVj+Ui/OVnL8b6FQsxuXPi0BUKgrGawuafv4jDZopps7RVa0VZ6uqoS0F9aNcBbN19wOie3t3hq+9araZQrdXw+ctPG3c97nvtDazd7L7PFiG6EqZTS7vQ013Exqv6xzvsuJXXhHWnenWiSTvEQPegB1aB39bdB3xXpje+p6aUp9lumfp/+8HT8IlvP42px3SPV6cPDo9ibRO3pB3rXNY55k7rhUjwrUcIIXqSZIIEswczSNlsJrnqD2bjj7/2xHjBsZXtd6S7ikkd9W37lVKYO60X7/vRAAZeGcYnL1rg2SzXqgub1FHA5y8/DR//1tN434af4dF178ZND+ya8Jk93UWUupy/NitAW6mpcRfj1hvelbo5TwiJBq8NI6N2p7q5FMM05o4SzloelDqLWLn0JBwzqQPTj+0eT26Yfmw3bjh/Pro7Cnh1eKSujmLjVWfUbVe95an9WLFoJj532amYNaU0YcUyPFrBDefPx5rNOzH1mO7xOJYIxj/zwR37AFsvv2q1hmJxonvSyQ+9dfcB167PhJBs4VUKYG+2m+cFKd2DTbAUy6GhUdz0wK66bELLpdfo+rvn6n78bqTiWABsKasjlSrGKjWM1Wro6+me4MKzeoM1Fjl7uiY9XAf2TEUGtAnJJjkvuqZ7MAqMTMIO9EwxrK33Lzlh3FV4/HGTHVc9nZ1FvLmj6GiqW4pleKQCALjpgV342w+e5mghXf/eeROKnK1tAZxuUq+Mq17TMsvBjU1I2+KUOdhunhNmD/rAKji+4fz56J1kZBN+fNkCz/6BbplPlk/6xL4STuwz0lQ/v/W5CY0rVy49CeWRSuCmmcy4IiS/hN2hPE9w2e2TUpfR3un5l4ew5ry5OP64yVj3jZ34mw+cNqF/oNeqx/JJDxwcQsa1rAwAAAiRSURBVHdHwbMuCzDcAUykIIRYpJ0IkTaZsbRE5CIReU5EBkTkU0l/vmVtWdmELx7y3yXZjpUs8ZUfD6Cnq4jPX360Lmv9lmcwODwyXkis47YAhBCSJplIxBCRIoA9AC4AsA/ANgArlVK/cHp9lIkYdqx41JSeLqz7pnOX9ilNdhW1J0tMP7YbN138VvR0dRhtlUaM1HmnBAvu3EsIyTm5SsQ4C8CAUmovAIjIZgCXAHBUWnFh+ZOHR527tDezsuznmJAsIYLeSc5fR7u7AwghxCIr7sHjAbxk+3ufeSxxrGxCpy7tkzr8ueyYLEEIIeHIyrLdaVav82uKyGoAqwFg1qxZsQrDZp6EEJIOWbG09gE40fb3CQD221+glLpTKdWvlOqfOnVq7ALRWiKEkOTJitLaBmCeiMwWkS4AVwDYkrJMhBBCEiYT7kGlVEVErgewFUARwN1KqWdSFosQQkjCZEJpAYBS6iEAD6UtByGEkPTIinuQEEIIodIihBCSHai0CCGEZAYqLUIIIZmBSosQQkhmoNIihBCSGai0CCGEZAYqLUIIIZmBSosQQkhmyMQmkEERkVcA/LqFU7wZwKsRiZMkWZQ7izIDlDtJsigzQLmD8qpS6qJmL8ql0moVEdmulOpPW46gZFHuLMoMUO4kyaLMAOWOC7oHCSGEZAYqLUIIIZmBSsuZO9MWICRZlDuLMgOUO0myKDNAuWOBMS1CCCGZgZYWIYSQzECl1YCIXCQiz4nIgIh8Km153BCRF0Rkl4jsFJHt5rE+EXlERJ43fx6ngZx3i8hBEdltO+YopxhsMMf+aRE5XTO514vIb8wx3ykiF9v+7yZT7udEZFlKMp8oIj8WkWdF5BkRWWse13q8PeTWdrxFZJKI/FxEnjJl/gvz+GwRedwc62+ISJd5vNv8e8D8/5OTlrmJ3PeIyK9sY73YPK7FPVKHUor/zH8AigB+CWAOgC4ATwF4W9pyucj6AoA3Nxz7WwCfMn//FIC/0UDOdwE4HcDuZnICuBjAwwAEwNsBPK6Z3OsB3Ojw2reZ90o3gNnmPVRMQeYZAE43fz8GwB5TNq3H20NubcfbHLNe8/dOAI+bY/hNAFeYx28H8Cfm738K4Hbz9ysAfCOlsXaT+x4AH3R4vRb3iP0fLa16zgIwoJTaq5QaBbAZwCUpyxSESwDca/5+L4BLU5QFAKCU+imAwYbDbnJeAuA+ZfCfAN4kIjOSkbQeF7nduATAZqXUiFLqVwAGYNxLiaKUOqCUetL8/TCAZwEcD83H20NuN1Ifb3PMhsw/O81/CsB5AL5tHm8ca+s7+DaA94qIJCTuOB5yu6HFPWKHSque4wG8ZPt7H7wfnjRRAH4gIk+IyGrz2HSl1AHAmAgATEtNOm/c5MzC+F9vuknutrlftZPbdD8tgbGSzsx4N8gNaDzeIlIUkZ0ADgJ4BIbF91ulVMVBrnGZzf//HYApyUps0Ci3Usoa61vNsf47Eek2j2kx1naotOpxWvnoml55jlLqdADLAVwnIu9KW6AI0H38vwrgLQAWAzgA4Ivmca3kFpFeAN8BcINS6nWvlzoc00lurcdbKVVVSi0GcAIMS++tTi8zf2ohMzBRbhE5BcBNAH4fwJkA+gB80ny5NnJbUGnVsw/Aiba/TwCwPyVZPFFK7Td/HgTwzzAempct0938eTA9CT1xk1Pr8VdKvWw+8DUAG3HUJaWN3CLSCWPi/7pS6gHzsPbj7SR3FsYbAJRSvwXwExgxnzeJSIeDXOMym///e/Dvfo4Fm9wXmS5apZQaAfBP0HSsASqtRrYBmGdmAHXBCJhuSVmmCYhIj4gcY/0O4EIAu2HIusp82SoA/5KOhE1xk3MLgKvMjKW3A/id5dbSgQZf/vthjDlgyH2FmSE2G8A8AD9PQT4BcBeAZ5VSX7L9l9bj7Sa3zuMtIlNF5E3m75MBnA8jFvdjAB80X9Y41tZ38EEAP1JmpkOSuMj9X7ZFjcCIw9nHOvV7pI60M0F0+wcjW2YPDP/0zWnL4yLjHBjZU08BeMaSE4aP/IcAnjd/9mkg6yYYrp0xGKu2j7nJCcMV8RVz7HcB6NdM7vtNuZ6G8TDPsL3+ZlPu5wAsT0nmd8Bw3TwNYKf572Ldx9tDbm3HG8BpAHaYsu0G8Ofm8TkwFOgAgG8B6DaPTzL/HjD/f05KY+0m94/Msd4N4Gs4mmGoxT1i/8eOGIQQQjID3YOEEEIyA5UWIYSQzEClRQghJDNQaRFCCMkMVFqEEEIyA5UWIQkiIv8R8n2XisjbWvjck0Xkf4Z9PyG6QKVFSIIopf4g5FsvhdHdPCwnA6DSIpmHdVqEJIiIDCmlekXkXBhbb7wK4BQATwC4UimlROSvAawAUAHwAwAPAPgujCarvwPwARjdxFfD2EJnAMCHlVJlEbkHwOsA+gH8NwCfUEp9W0T+E0ZvvF8BuFcp9XfJXDEh0dLR/CWEkJhYAmAhjF5u/w7gHBH5BYyWRb9vKrA3KaV+KyJbAHxXKfVtABCR3yqlNpq/3wKjY8ffm+edAaPLxO/D6CTxbRj7aN2olPrvyV0eIdFD9yAh6fFzpdQ+ZTSE3QnDhfc6gCMA/lFELgNQdnnvKSLyMxHZBeBDMJSfxYNKqZpS6hcApscnPiHJQ6VFSHqM2H6vAuhQxl5LZ8HoeH4pgO+7vPceANcrpU4F8Bcwets5nTfxjQYJiRO6BwnRCHNPqZJS6iEzDjVg/tdhGFvRWxwD4IC5pceHAPymyakb309IJqGlRYheHAPguyLyNIB/BfC/zeObAXxcRHaIyFsA/L8wdvd9BMB/+Tjv0wAqIvKUiPzvpq8mRFOYPUgIISQz0NIihBCSGai0CCGEZAYqLUIIIZmBSosQQkhmoNIihBCSGai0CCGEZAYqLUIIIZmBSosQQkhm+P8B+7Q7d5zcPcQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88f089f5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot of only the highly correlated pairs\n",
    "for v,i,j in s_corr_list:\n",
    "    sns.pairplot(data, size=6, x_vars=cols[i],y_vars=cols[j] )\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 从原始数据中分离输入特征x和输出y\n",
    "#日期也应该去掉，否则下面的数据标准化会出错，无法将日期转成数字\n",
    "#去掉atemp，保留temp，反过来也可以，他们相关性系数为1，应该做降维处理\n",
    "#去掉类别特征，因为感觉会对权重和Lasso结果产生一定影响\n",
    "data.drop(['dteday'],axis=1,inplace=True) \n",
    "data.drop(['atemp'],axis=1,inplace=True) \n",
    "data.drop(['yr'],axis=1,inplace=True) \n",
    "data.drop(['instant'],axis=1,inplace=True) \n",
    "data.drop(['mnth'],axis=1,inplace=True) \n",
    "data.drop(['season'],axis=1,inplace=True) \n",
    "y = data['cnt'].values\n",
    "X = data.drop('cnt', axis = 1)\n",
    "\n",
    "#用于后续显示权重系数对应的特征\n",
    "columns = X.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "划分训练集和测试集，这里由于我一开始就是用的2011年的数据，所以2012年的数据我一直保持不动"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(244, 9)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将数据分割训练数据与测试数据\n",
    "from sklearn.model_selection import train_test_split\n",
    "#random_state是种子，如果给定数据，那么种子不变，如果为零或者不填，那么每次都变\n",
    "#test_size如果是整数，那么就是训练数据的个数\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33, test_size=0.3)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/guanyu/.local/lib/python3.6/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 分别初始化对特征和目标值的标准化器\n",
    "ss_X = StandardScaler()\n",
    "ss_y = StandardScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征以及目标值进行标准化处理\n",
    "X_train = ss_X.fit_transform(X_train)\n",
    "X_test = ss_X.transform(X_test)\n",
    "\n",
    "#对y做标准化不是必须\n",
    "#对y标准化的好处是不同问题的w差异不太大，同时正则参数的范围也有限\n",
    "y_train = ss_y.fit_transform(y_train.reshape(-1, 1))\n",
    "y_test = ss_y.transform(y_test.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "确定模型类型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "缺省参数线性回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[0.7863842795937613]</td>\n",
       "      <td>registered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[0.39643987635370387]</td>\n",
       "      <td>casual</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[7.840950111415168e-16]</td>\n",
       "      <td>temp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[1.6653345369377348e-16]</td>\n",
       "      <td>workingday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[5.551115123125783e-17]</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[6.938893903907228e-18]</td>\n",
       "      <td>weathersit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[-7.380813687368763e-17]</td>\n",
       "      <td>holiday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[-1.0234868508263162e-16]</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[-3.677613769070831e-16]</td>\n",
       "      <td>hum</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        coef     columns\n",
       "8       [0.7863842795937613]  registered\n",
       "7      [0.39643987635370387]      casual\n",
       "4    [7.840950111415168e-16]        temp\n",
       "2   [1.6653345369377348e-16]  workingday\n",
       "1    [5.551115123125783e-17]     weekday\n",
       "3    [6.938893903907228e-18]  weathersit\n",
       "0   [-7.380813687368763e-17]     holiday\n",
       "6  [-1.0234868508263162e-16]   windspeed\n",
       "5   [-3.677613769070831e-16]         hum"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 线性回归\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# 使用默认配置初始化\n",
    "lr = LinearRegression()\n",
    "\n",
    "# 训练模型参数\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "# 预测\n",
    "y_test_pred_lr = lr.predict(X_test)\n",
    "y_train_pred_lr = lr.predict(X_train)\n",
    "\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef\":list((lr.coef_.T))})\n",
    "fs.sort_values(by=['coef'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "模型评价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LinearRegression on test is 1.0\n",
      "The r2 score of LinearRegression on train is 1.0\n"
     ]
    }
   ],
   "source": [
    "# 使用r2_score评价模型在测试集和训练集上的性能，并输出评估结果\n",
    "#测试集\n",
    "print ('The r2 score of LinearRegression on test is', r2_score(y_test, y_test_pred_lr))\n",
    "#训练集\n",
    "print ('The r2 score of LinearRegression on train is', r2_score(y_train, y_train_pred_lr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "得出了1.0的结果，考虑应该是注册用户数在权重当中的比重太高,我之前的代码也曾经试验过去掉注册用户数来进行评价，但是最终预测的结果和实际值（2012年实际cnt）相差太大，而且仅仅因为权重高就删掉一个强相关的特征，我觉得说不出道理，所以这里还是保留了这个结果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88f04c2860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#在训练集上观察预测残差的分布，看是否符合模型假设：噪声为0均值的高斯噪声\n",
    "f, ax = plt.subplots(figsize=(7, 5)) \n",
    "f.tight_layout() \n",
    "ax.hist(y_train - y_train_pred_lr,bins=40, label='Residuals Linear', color='b', alpha=.5); \n",
    "ax.set_title(\"Histogram of Residuals\") \n",
    "ax.legend(loc='best');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "整体趋近于高斯分布（正态分布），右skew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88f065bb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#观察预测值与真值的散点图\n",
    "plt.figure(figsize=(4, 3))\n",
    "plt.scatter(y_train, y_train_pred_lr)\n",
    "plt.plot([-3, 3], [-3, 3], '--k')   #数据已经标准化，3倍标准差即可\n",
    "plt.axis('tight')\n",
    "plt.xlabel('True number of bikes-renters')\n",
    "plt.ylabel('Predicted number of bikes-renters')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于数据量只有365，这里不准备使用随机梯度下降进行参数优化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "正则化线性回归，L2->岭回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is 0.9999999962590898\n",
      "The r2 score of RidgeCV on train is 0.9999999967596804\n"
     ]
    }
   ],
   "source": [
    "#岭回归／L2正则\n",
    "from sklearn.linear_model import  RidgeCV\n",
    "\n",
    "#设置超参数（正则参数）范围\n",
    "alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "#生成一个RidgeCV实例\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)  \n",
    "\n",
    "#模型训练\n",
    "ridge.fit(X_train, y_train)    \n",
    "\n",
    "#预测\n",
    "y_test_pred_ridge = ridge.predict(X_test)\n",
    "y_train_pred_ridge = ridge.predict(X_train)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print ('The r2 score of RidgeCV on test is', r2_score(y_test, y_test_pred_ridge))\n",
    "print ('The r2 score of RidgeCV on train is', r2_score(y_train, y_train_pred_ridge))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88f299dc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 0.01\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[0.7863842795937613]</td>\n",
       "      <td>[0.7862987375936936]</td>\n",
       "      <td>registered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[0.39643987635370387]</td>\n",
       "      <td>[0.3964100877084965]</td>\n",
       "      <td>casual</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[7.840950111415168e-16]</td>\n",
       "      <td>[7.335916967103444e-05]</td>\n",
       "      <td>temp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[1.6653345369377348e-16]</td>\n",
       "      <td>[6.130249293384571e-06]</td>\n",
       "      <td>workingday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[5.551115123125783e-17]</td>\n",
       "      <td>[8.801597834451336e-07]</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[6.938893903907228e-18]</td>\n",
       "      <td>[-2.1755781336250946e-05]</td>\n",
       "      <td>weathersit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[-7.380813687368763e-17]</td>\n",
       "      <td>[-3.0718542404356564e-06]</td>\n",
       "      <td>holiday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[-1.0234868508263162e-16]</td>\n",
       "      <td>[-1.9689438915886093e-05]</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[-3.677613769070831e-16]</td>\n",
       "      <td>[2.5097833455806073e-06]</td>\n",
       "      <td>hum</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     coef_lr                 coef_ridge     columns\n",
       "8       [0.7863842795937613]       [0.7862987375936936]  registered\n",
       "7      [0.39643987635370387]       [0.3964100877084965]      casual\n",
       "4    [7.840950111415168e-16]    [7.335916967103444e-05]        temp\n",
       "2   [1.6653345369377348e-16]    [6.130249293384571e-06]  workingday\n",
       "1    [5.551115123125783e-17]    [8.801597834451336e-07]     weekday\n",
       "3    [6.938893903907228e-18]  [-2.1755781336250946e-05]  weathersit\n",
       "0   [-7.380813687368763e-17]  [-3.0718542404356564e-06]     holiday\n",
       "6  [-1.0234868508263162e-16]  [-1.9689438915886093e-05]   windspeed\n",
       "5   [-3.677613769070831e-16]   [2.5097833455806073e-06]         hum"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_mean = np.mean(ridge.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas),1)) \n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "\n",
    "print ('alpha is:', ridge.alpha_)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef_lr\":list((lr.coef_.T)), \"coef_ridge\":list((ridge.coef_.T))})\n",
    "fs.sort_values(by=['coef_lr'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "和下面的Lasso回归结果比较，岭回归得出的结论很接近"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "正则化线性回归，L1->Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on test is 0.9999986872387459\n",
      "The r2 score of LassoCV on train is 0.9999987399154296\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/guanyu/.local/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:1094: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "#### Lasso／L1正则\n",
    "# class sklearn.linear_model.LassoCV(eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, \n",
    "#                                    normalize=False, precompute=’auto’, max_iter=1000, \n",
    "#                                    tol=0.0001, copy_X=True, cv=None, verbose=False, n_jobs=1,\n",
    "#                                    positive=False, random_state=None, selection=’cyclic’)\n",
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "lasso = LassoCV()  \n",
    "\n",
    "#训练（内含CV）\n",
    "lasso.fit(X_train, y_train)  \n",
    "\n",
    "#测试\n",
    "y_test_pred_lasso = lasso.predict(X_test)\n",
    "y_train_pred_lasso = lasso.predict(X_train)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print ('The r2 score of LassoCV on test is', r2_score(y_test, y_test_pred_lasso))\n",
    "print ('The r2 score of LassoCV on train is', r2_score(y_train, y_train_pred_lasso))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88f06a2278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 0.0009290849890741202\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_lasso</th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.785698</td>\n",
       "      <td>[0.7863842795937613]</td>\n",
       "      <td>[0.7862987375936936]</td>\n",
       "      <td>registered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.395765</td>\n",
       "      <td>[0.39643987635370387]</td>\n",
       "      <td>[0.3964100877084965]</td>\n",
       "      <td>casual</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[7.840950111415168e-16]</td>\n",
       "      <td>[7.335916967103444e-05]</td>\n",
       "      <td>temp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>[1.6653345369377348e-16]</td>\n",
       "      <td>[6.130249293384571e-06]</td>\n",
       "      <td>workingday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>[5.551115123125783e-17]</td>\n",
       "      <td>[8.801597834451336e-07]</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>[6.938893903907228e-18]</td>\n",
       "      <td>[-2.1755781336250946e-05]</td>\n",
       "      <td>weathersit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>[-7.380813687368763e-17]</td>\n",
       "      <td>[-3.0718542404356564e-06]</td>\n",
       "      <td>holiday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>[-1.0234868508263162e-16]</td>\n",
       "      <td>[-1.9689438915886093e-05]</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[-3.677613769070831e-16]</td>\n",
       "      <td>[2.5097833455806073e-06]</td>\n",
       "      <td>hum</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   coef_lasso                    coef_lr                 coef_ridge  \\\n",
       "8    0.785698       [0.7863842795937613]       [0.7862987375936936]   \n",
       "7    0.395765      [0.39643987635370387]       [0.3964100877084965]   \n",
       "4    0.000000    [7.840950111415168e-16]    [7.335916967103444e-05]   \n",
       "2   -0.000000   [1.6653345369377348e-16]    [6.130249293384571e-06]   \n",
       "1   -0.000000    [5.551115123125783e-17]    [8.801597834451336e-07]   \n",
       "3   -0.000000    [6.938893903907228e-18]  [-2.1755781336250946e-05]   \n",
       "0   -0.000000   [-7.380813687368763e-17]  [-3.0718542404356564e-06]   \n",
       "6   -0.000000  [-1.0234868508263162e-16]  [-1.9689438915886093e-05]   \n",
       "5    0.000000   [-3.677613769070831e-16]   [2.5097833455806073e-06]   \n",
       "\n",
       "      columns  \n",
       "8  registered  \n",
       "7      casual  \n",
       "4        temp  \n",
       "2  workingday  \n",
       "1     weekday  \n",
       "3  weathersit  \n",
       "0     holiday  \n",
       "6   windspeed  \n",
       "5         hum  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses) \n",
    "#plt.plot(np.log10(lasso.alphas_)*np.ones(3), [0.3, 0.4, 1.0])\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()    \n",
    "            \n",
    "print ('alpha is:', lasso.alpha_)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef_lr\":list((lr.coef_.T)), \"coef_ridge\":list((ridge.coef_.T)), \"coef_lasso\":list((lasso.coef_.T))})\n",
    "fs.sort_values(by=['coef_lr'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这样就完成了回归分析。从分析结果来看，非注册用户数和注册用户数起着主要影响，其余为次要影响。\n",
    "其实之前做的时候保留了类别特征并且去掉了注册用户数这个特征，那样的话，温度和非注册用户数起的作用比较明显，但是我觉得没什么道理删除注册用户数，如果不对，希望讲解。\n",
    "另外这样一来，几乎是说，除了注册用户数和非注册用户数，其余特征都是可以忽略不计的了。。。\n",
    "下面应该对2012年的y进行预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = pd.read_csv(\"day.csv\")\n",
    "data1 = data1[data1[\"yr\"]==1]  ##筛选出2012年的数据\n",
    "##去掉类别特征，使得X和训练集模型的X一致\n",
    "data1.drop(['dteday'],axis=1,inplace=True) \n",
    "data1.drop(['atemp'],axis=1,inplace=True) \n",
    "data1.drop(['yr'],axis=1,inplace=True) \n",
    "data1.drop(['instant'],axis=1,inplace=True) \n",
    "data1.drop(['mnth'],axis=1,inplace=True) \n",
    "data1.drop(['season'],axis=1,inplace=True) \n",
    "X = data1.drop('cnt', axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里决定还是使用Lassor回归来进行最后的预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1536.46367677]\n",
      " [1439.0892951 ]\n",
      " [1723.65019728]\n",
      " [1825.11325577]\n",
      " [2518.45714638]\n",
      " [3102.88984598]\n",
      " [3138.00281658]\n",
      " [2459.78946007]\n",
      " [1827.11494157]\n",
      " [2761.95025622]\n",
      " [1676.08369158]\n",
      " [3116.92134902]\n",
      " [2459.58874845]\n",
      " [1830.60452275]\n",
      " [1706.58985962]\n",
      " [1722.493139  ]\n",
      " [2258.5149214 ]\n",
      " [2612.32938796]\n",
      " [2538.084276  ]\n",
      " [2442.48986998]\n",
      " [ 996.95967273]\n",
      " [1478.25261772]\n",
      " [1855.9446295 ]\n",
      " [3240.93579613]\n",
      " [3175.75683755]\n",
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      " [2612.8490258 ]\n",
      " [2861.41704429]\n",
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      " [5392.93029267]\n",
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      " [  16.52056534]\n",
      " [ 827.95200735]\n",
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      " [4175.73235396]\n",
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      " [3914.56010838]\n",
      " [3312.09946164]\n",
      " [4149.24211643]\n",
      " [4222.2587199 ]\n",
      " [3806.9176948 ]\n",
      " [1534.58497292]\n",
      " [2449.68165482]\n",
      " [1583.14658211]\n",
      " [1785.70267313]\n",
      " [3868.9255664 ]\n",
      " [3065.33220131]\n",
      " [4059.17231882]\n",
      " [4091.16703029]\n",
      " [4316.06622276]\n",
      " [3711.28366789]\n",
      " [3308.07010814]\n",
      " [4685.90045519]\n",
      " [4979.99518481]\n",
      " [4376.12394032]\n",
      " [4094.23440571]\n",
      " [3802.12187547]\n",
      " [3939.99115176]\n",
      " [2366.4829727 ]\n",
      " [3937.31501683]\n",
      " [4215.93560033]\n",
      " [4061.89521815]\n",
      " [4184.55146334]\n",
      " [4245.11604381]\n",
      " [3669.79410182]\n",
      " [2767.4607936 ]\n",
      " [3522.90370845]\n",
      " [4201.0915151 ]\n",
      " [4012.03451434]\n",
      " [3123.75176355]\n",
      " [2762.89253185]\n",
      " [1295.44750235]\n",
      " [1246.17139111]\n",
      " [ 655.62321106]\n",
      " [ 625.0317378 ]\n",
      " [ 343.28596767]\n",
      " [1566.10009946]\n",
      " [2182.73514966]\n",
      " [ 992.54015882]\n",
      " [1270.40640337]\n",
      " [1974.85710599]]\n"
     ]
    }
   ],
   "source": [
    "y=lr.predict(X)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "                                    这里就是最终预测的2012年的y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "365    2294\n",
      "366    1951\n",
      "367    2236\n",
      "368    2368\n",
      "369    3272\n",
      "370    4098\n",
      "371    4521\n",
      "372    3425\n",
      "373    2376\n",
      "374    3598\n",
      "375    2177\n",
      "376    4097\n",
      "377    3214\n",
      "378    2493\n",
      "379    2311\n",
      "380    2298\n",
      "381    2935\n",
      "382    3376\n",
      "383    3292\n",
      "384    3163\n",
      "385    1301\n",
      "386    1977\n",
      "387    2432\n",
      "388    4339\n",
      "389    4270\n",
      "390    4075\n",
      "391    3456\n",
      "392    4023\n",
      "393    3243\n",
      "394    3624\n",
      "       ... \n",
      "701    4649\n",
      "702    6234\n",
      "703    6606\n",
      "704    5729\n",
      "705    5375\n",
      "706    5008\n",
      "707    5582\n",
      "708    3228\n",
      "709    5170\n",
      "710    5501\n",
      "711    5319\n",
      "712    5532\n",
      "713    5611\n",
      "714    5047\n",
      "715    3786\n",
      "716    4585\n",
      "717    5557\n",
      "718    5267\n",
      "719    4128\n",
      "720    3623\n",
      "721    1749\n",
      "722    1787\n",
      "723     920\n",
      "724    1013\n",
      "725     441\n",
      "726    2114\n",
      "727    3095\n",
      "728    1341\n",
      "729    1796\n",
      "730    2729\n",
      "Name: cnt, Length: 366, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "col=data1.loc[:,'cnt'] \n",
    "print (col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88e92b2e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_real = col\n",
    "plt.figure(figsize=(4, 3))\n",
    "plt.scatter(y_real, y)\n",
    "plt.plot([-3, 3], [-3, 3], '--k')   #数据已经标准化，3倍标准差即可\n",
    "plt.axis('tight')\n",
    "plt.xlabel('True number of bikes-renters')\n",
    "plt.ylabel('Predicted number of bikes-renters')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "感觉还是差了一些，但是我也试验过用去掉注册用户数的模型来预测结果，那样的话，预测值与实际值有6倍左右的偏差，感觉这个还算贴近了。。。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
